speech on computer in urdu

• Voice to Text
• Urdu Voice Typing

Urdu (اردو) Voice Typing

Note: Click on the Mic icon and Start Speak.

No speech was detected. You may need to adjust your microphone settings.

No microphone was found. Ensure that a microphone is installed and that microphone settings are configured correctly.

Click the "Allow" button above to enable your microphone.

Permission to use microphone was denied.

Permission to use microphone is blocked. To change, go to chrome://settings/contentExceptions#media-stream

Speech Recognition is not supported by this browser. Upgrade to Chrome version 25 or later.

Note : This feature currently works only on Google Chrome browser. You can download and install Google Chrome. Download Google Chrome

Urdu (اردو) voice typing is an easy method of typing. This is a very good option for those who want to write Urdu without using any keyboard. All you need is a good mic, set up the mic in your computer and start speaking, the Voice to Text typing tool will recognize your voice and automatically start typing Urdu. After voice typing, you can copy it and use it on MS Word, social media, comments, Urdu articles etc. Please share it on Facebook, Twitter and WhatsApp and help us reach more users.

Instruction

• You must have a good quality mic.
• You have to speak loud and clear.

RELATED LINKS

• 👉 Urdu to English Translation
• 👉 English to Urdu Translation

Use Dictation to Type in Urdu

Have you ever wanted to be able to use your voice to type on websites like Gmail or Facebook, without having to use your keyboard? Voice In is a Chrome extension that lets you do exactly that. Voice In lets you dictate in over 50 languages, including Urdu! .

Add to Chrome – it’s free

Here's how to use Voice In to type in Urdu

(1) Install the Voice In - Voice Typing extension from the Chrome Web Store . (2) On install, it will open a setup page. In the setup, select "Urdu" from the list of available languages. (3) After setup, open the web page where you want to use your voice to type. (4) Click on the Voice In icon in your Chrome toolbar to activate the extension, place cursor in the text box, and start speaking. Voice In will transcribe your voice into text on the web page.

Dialects supported

Voice In supports the following dialects of Urdu:

Voice Commands

You can also define your own custom voice commands with Voice In Plus . Learn more about custom voice commands. ** We are looking for volunteers to help us add voice commands for more languages. Please reach out at [email protected]

Urdu with Pakistani Accent Text to Speech Generation

Language code: ur-PK

The Urdu language, with a Pakistani accent, carries a unique musicality and rhythm. Native to Pakistan and one of its official languages.

Here are some pronunciation features of Urdu as it is spoken with a Pakistani accent:

Influence of Other Languages. Given Pakistan's multilingual society, Pakistani Urdu has influences from Punjabi, Sindhi, Pashto, and other regional languages. These influences can cause variations in pronunciation, vocabulary, and syntax, leading to a distinctive Pakistani accent.

Consonant Sounds. The Urdu language consists of various aspirated and unaspirated consonants, which can be dental, retroflex, or palatal in articulation. In the Pakistani accent, the retroflex consonants are pronounced with a curling of the tongue tip back in the mouth, which is not commonly found in the English language.

Vowel Sounds. This language is known for its unique system of vowel harmony, where the type of vowel in the primary syllable often affects the rest of the word's vowel sounds. With a Pakistani inflection, vowel duration and pronunciation might display some variation, capable of altering a word's meaning.

Stress Patterns. The usual tendency is to place stress on the final syllable of a word. Yet, the stress placement may occasionally shift when spoken with a Pakistani intonation, contingent upon the word's positioning within a sentence.

SpeechGen offers an advanced text to speech solution in Urdu that captures the essence of the Pakistani accent. Our advanced algorithm is built on a neural network that flawlessly transforms your text into a voice that sounds natural and real.

Our cutting-edge service goes beyond merely reading out your text. It makes every effort to capture the rhythm, intonation, and unique articulation characteristics of the Pakistani accent.

Other Accents

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• Voice to Text
• Urdu Voice Typing

Urdu Voice Typing | Urdu (اردو) Speech To Text | Urdu Voice To Text

Note: Click on the Mic icon and Start Speak.

No speech was detected. You may need to adjust your microphone settings.

No microphone was found. Ensure that a microphone is installed and that microphone settings are configured correctly.

Click the "Allow" button above to enable your microphone.

Permission to use microphone was denied.

Permission to use microphone is blocked. To change, go to chrome://settings/contentExceptions#media-stream

Speech Recognition is not supported by this browser. Upgrade to Chrome version 25 or later.

How to Convert Urdu (اردو) Voice Typing

Using this steps to convert urdu (اردو) voice into text..

step: 1 Urdu speech to text convert click microphone icon on textarea and start speaking as long as you like.

step: 2 You can also use a keyboard to add special words and add text anything in your native language also correct copywriting.

step: 3 Finally speaking Microphone after stop and then result to print copy or email.

step: 1 Gives good performance in low noise.

step: 2 Also Without log-in you convert any languages as well free

step: 3 Supportd all languages to Like Italian, Chinese, Hindi, French, Indonesian, Arabic, Malay, German and more.

Here Some Common Word In Daily Routing Write In Urdu language

Related links.

• 👉 Urdu to English Translation
• 👉 English to Urdu Translation

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Urdu Text to Speech

Create professional voiceovers with lovo's urdu text to speech voices.

Enhance your content using LOVO's TTS voices to create top-notch voiceovers for your videos, marketing materials, presentations, and more.

How Urdu Text to Speech works

Step 1: Type or input text

Type text or simply copy and paste your desired text into the TTS blocks.

Step 2: Generate

Choose an AI voice from the wide range of 500+ voices in 100+ languages avaialble. Click generate and wait a few seconds and your speech is created by AI voices.

Step 3: Output speech

Within seconds, you'll have speech at the click of a button. No more spending time on logistics, just think and create.

Try Genny for free

Fast & cost-effective

Pro-grade tts voiceovers: save time, money.

Experience lightning-fast professional voice generation with LOVO's Urdu voice generator. Our TTS converter ensures you never have to waste time or money on re-recording. Make edits and update outdated content in just minutes with LOVO. Achieve faster creation and seamless project updates with ease – all with a couple of clicks.

Create in one place

All-in-one video editor & urdu voice generator..

Seamlessly produce high-quality videos with Genny: Generate Urdu TTS, create and edit videos, and access powerful AI tools – all in one user-friendly platform. Convert text to speech, upload videos, and leverage our intuitive timeline editor to craft exceptional videos without the need for expert editing skills.

Boost efficiency

Generate tts with a genuine urdu accent in record time..

Experience unparalleled speed with LOVO's Urdu text-to-speech generator. Create content in minimal time as it boasts the fastest generation speeds. Additionally, our rapid TTS generator supports over 100 languages and accents, enabling you to convert easily. Simply choose the desired voice for your script, click "generate," and within seconds, enjoy your Urdu voiceover to seamlessly incorporate into your project.

How do you convert Urdu text to voice?

What is the most realistic text to speech, what other text to speech languages are available in genny, how do i select voices in other languages, do i have commercial rights for urdu tts generated in genny.

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Text to Speech

متن سے تقریر

90 زبانوں میں 700 حقیقی اسپیچ سنتھیسز والی آوازوں کے ساتھ تحریر کو آواز میں تبدیل کریں۔

آڈیو بنائیں ہمارا اردو ٹیکسٹ ریڈنگ پروگرام مفت میں آزمائیں۔ رجسٹریشن کی ضرورت نہیں ہے۔

اردو اسپیچ سنتھیسائزر مختلف قسم کی دستاویزات جیسے Word، PDF اور EPUB سے تحریر کو بلند آواز سے پڑھنا آسان بناتا ہے۔ Narakeet کے ساتھ MP3 کے لیے تحریر کو آواز میں تبدیل کریں، Powerpoint تحریر سے اسپیچ پریزنٹیشنز میں ویڈیوز بنائیں اور آسانی سے آڈیو اور ویڈیو پروڈکشن کو خودکار بنائیں۔

Narakeet قدرتی AI سنتھیسز کے ذریعہ تحریر کو بلند آواز سے پڑھنے کی ایک ایپلی کیشن ہے۔ حقیقی تحریر سے آواز کی تبدیلی کا استعمال کرتے ہوئے، آپ آسانی سے ویڈیو میں آڈیو شامل کر سکتے ہیں، تحریر کو اردو وائس اوور میں تبدیل کر سکتے ہیں، زبان کے اسباق کو آن لائن بنا سکتے ہیں اور بھی بہت کچھ کر سکتے ہیں۔

• اردو تحریر سے آواز کی خدمت

Narakeet کے پاس 2 حقیقی اردو اسپیچ سنتھیسائزر ہیں۔

فوری ڈیمو یا اردو زبان میں تحریر سے آواز میں تبدیلی کرنے کے لیے نیچے دی گئی ویڈیو (آواز کے ساتھ) چلائیں۔

آسانی سے تحریر کو آواز میں بدلیں

Narakeet ایک آن لائن ٹیکسٹ ریڈر ہے۔ یہ تحریر کو آواز میں تبدیل کرنا اور تحریر سے حقیقی انسان جیسی آواز میں تحریر کو آواز میں تبدیل کرکے آڈیو فائلز بنانا آسان بناتا ہے۔ ہمارے ٹیکسٹ ٹو اسپیچ کے ساتھ مفت میں شروع کریں۔ ذیل میں ہمارے اردو زبان کے سنتھیسائزر میں سے کسی ایک کو منتخب کریں اور آڈیو بنانے کے لیے کوئی تحریر درج کریں۔

مزید آپشنز کے لیے (ٹیکسٹ ٹو اسپیچ Word دستاویزات، PDF سے تحریر کو پڑھنا، آواز کی رفتار/ والیوم کنٹرولز، Powerpoint فائلز یا مارک ڈاؤن اسکرپٹس کے ساتھ کام کرنا)، ہمارے ٹولز کا صفحہ ملاحظہ کریں۔

ان آوازوں کے علاوہ، Narakeet کے پاس 90 زبانوں میں 700 اسپیچ سنتھیسز والی آوازیں ہیں۔

اردو AI اسپیچ سنتھیسائزر

Narakeet ایک ٹیکسٹ ٹو اسپیچ ایپلی کیشن ہے۔ یہ آپ کو بہت سے مقاصد کے لیے آڈیو اور ویڈیو فائلز آسانی سے بنانے میں مدد کر سکتی ہے۔ یہ وہ چند کام ہیں جو آپ ہمارے کمپیوٹر اسپیچ سنتھیسائزر کے ساتھ کر سکتے ہیں:

• انگریزی اسپیچ سنتھیسز
• ہندی آوازیں بنانے کی خدمت
• ٹیکسٹ ٹو اسپیچ آڈیو بکس
• ویڈیوز کے لیے اردو وائس اوور
• ٹیکسٹ ٹو وائس اردو YouTube ویڈیوز

اسپیچ سنتھیسز کا استعمال کیسے کریں؟

کسی بھی ویب براؤزر میں Narakeet آٹومیٹک ٹیکسٹ ریڈر کھولیں، کوئی تحریر ٹائپ کریں اور ہمارے اردو آواز کی سنتھیسز کے آپشنز میں سے ایک کو منتخب کریں۔ چند لمحوں میں، آپ تحریر کو آواز میں تبدیل کر پائیں گے اور پیشہ ورانہ آواز کی آڈیو حاصل کریں گے۔ یہ آڈیو خود ریکارڈ کرنے یا اردو آواز کے اداکاروں کی خدمات حاصل کرنے سے کہیں زیادہ تیز اور آسان ہے۔

اردو تحریر کو بلند آواز سے کیسے پڑھیں؟

بلند آواز سے اردو تحریر کو پڑھنے کے لیے، بس “اسکرپٹ” فیلڈ میں تحریر درج کریں، اردو آوازوں میں سے کسی ایک کا انتخاب کریں اور “آڈیو بنائیں” بٹن پر کلک کریں۔ Narakeet بہترین آن لائن ٹیکسٹ ٹو اسپیچ پروگرام ہے اور آپ اسے کسی بھی ویب براؤزر سے استعمال کر سکتے ہیں۔ تحریر کو پڑھوانے کے لیے مفت میں Narakeet کا استعمال شروع کریں۔

اسپیچ سنتھیسز کیسے کام کرتا ہے؟

اسپیچ سنتھیسز ایپلی کیشن کمپیوٹرائزڈ آواز کا استعمال کرتے ہوئے اونچی آواز میں تحریر کو پڑھتی ہے اور Narakeet جیسے اعلیٰ معیار والے الیکٹرانک ٹیکسٹ ریڈرز اس تحریر سے آڈیو تیار کر سکتے ہیں جو حقیقت پسندانہ ہوتی ہے۔ بالکل ویسے ہی جیسے کوئی مقامی شخص بولتا ہے۔ اس طرح کے ٹولز آن لائن اسپیچ سنتھیسز، ویب پیجز یا کتابوں کے خودکار آڈیو ورژن بنانے اور مارکیٹنگ یا معلوماتی ویڈیوز بنانے میں لوگوں کی مدد کے لیے استعمال ہوتے ہیں۔ آج کل ٹیکسٹ ٹو وائس ٹولز کے لیے ویڈیو وائس اوور خاص طور پر عام استعمال کا معاملہ ہے کیونکہ آن لائن سامعین ٹیکسٹ پڑھنے پر ویڈیوز دیکھنے کو ترجیح دیتے ہیں۔

Word دستاویز کو بلند آواز سے کیسے پڑھیں؟

Word دستاویز کو بلند آواز سے پڑھنے کے لیے Narakeet ٹیکسٹ ٹو اسپیچ پروگرام کا استعمال کریں۔ اپنے Word دستاویز کو اپ لوڈ کریں، پھر اردو ٹیکسٹ ٹو اسپیچ آواز منتخب کریں اور “آڈیو بنائیں” بٹن پر کلک کریں۔

کیا کوئی اردو وائس API ہے؟

Narakeet کے پاس ایک وائس سنتھیسز API ہے جو آپ کو تحریر کو آواز میں تبدیل کرنے اور خود بخود اردو تحریر کو بلند آواز میں پڑھنے میں مدد فراہم کرتا ہے۔ مزید معلومات کے لیے، ہمارا خودکار ٹیکسٹ ٹو اسپیچ کنورژن والا صفحہ دیکھیں ۔

Narakeet helps you create text to speech voiceovers , turn Powerpoint presentations and Markdown scripts into engaging videos. It is under active development, so things change frequently. Keep up to date: RSS , Slack , Twitter , YouTube , Facebook , Instagram , TikTok

Urdu Text To Speech

Achieve outstanding outcomes with Speakatoo's Urdu Text to Speech tool, offering 850+ AI voices for precise voice selection.

Signup to download file

How to Convert Urdu Text to Speech?

Convert Urdu text to speech using Speakatoo by following these simple steps for natural results.

1. Choose the Urdu language

Select the Urdu language from the list or experience Speakatoo's text to speech conversion in 130+ languages.

2. Select any Male/Female Voice

Choose a voice tone, preview it, and toggle between options to find the right one before converting text to speech.

3. Type your content

Paste or type your text content for the conversion within the character limit. 

4. Set Audio Control or Voice Effects

Adjust Rate, Pitch, or Volume in Audio Control. Apply voice effects such as Angry, Cheerful, Excited, Shouting, Whispering, and more.

5. Choose desired output file format

Create output files in formats like mp3, wav, mp4, ogg, and flac. Choose the format that suits your needs.

6. Click on Synthesize & Download

Our online AI voice generator will convert your text into high quality audio in just a few seconds. You can download your audio file from the list.

Why Choose Us

Additional Urdu Voice-over Features

Audio controls.

Speakatoo's Urdu text to speech converter empowers users to create highly expressive and engaging Urdu content by adjusting audio controls. Speakatoo Urdu text to speech supports controls like

Speakatoo TTS converter provides you with some add-on features like AI Writer, which allows you to generate high-quality content. This can be useful for creating blog posts, articles, scripts, and other types of content.

Typing Master

Speakatoo Typing Master, is a Transliteration tool allowing you to type in any language using your regular keyboard. The process of transliterating to Urdu is very quick, it supports unlimited characters. For example, type "Aap Kasai hai?" and watch it seamlessly transform into "आप कैसे हैं?" Copy or download your Urdu text with ease.

API Integration

Speakatoo also offers API integration, enabling you to seamlessly integrate the converter into your applications. This can be beneficial in developing speech synthesis applications, voice assistants, and accessibility features.

Usecase of Urdu Text to Speech Converter

Urdu Audio Articles

Convert Urdu text to speech for fast, valuable audio marketing.

Urdu Voice MP3 Files

Create engaging Urdu voice MP3 files for impactful audio content.

Urdu Voice Messages

Send meaningful Urdu voice messages for effective communication and connection.

Urdu Podcast

Produce compelling Urdu podcasts with authentic, text to speech Urdu voices.

Urdu Social Media Video

Enhance engagement with Urdu social media videos using text to voice technology.

Urdu Language Lessons

Learn Urdu through interactive language lessons with text to speech in Urdu.

Preview Text to Speech in Urdu Accents

  گل,   سلمان,   اسد,   عظمیٰ.

Unlock the power of Speakatoo to effortlessly convert your Urdu text into engaging voices. Explore our extensive library of 850+ AI-generated Urdu voices, with options for both male and female speakers. Conveniently download your text-to-speech files in MP3 and WAV formats.

With Speakatoo's Urdu text to speech converter, you can elevate your audio content by customizing SSML parameters such as speed, pitch, and volume. Tailor your output to perfection and infuse your content with a personal touch using our advanced Urdu text to speech tool.

Frequently Asked Questions

What is Speakatoo's Urdu text to speech and how does it work?

Speakatoo's Urdu text to speech (Urdu TTS) software converts any text content into natural sounding Human-like voice. In other words, it allows users to generate computerized voice audio files from typed-out text. These files can be used in various applications such as e-learning, video and audio production, presentations, and more.

Does Speakatoo support human emotions in the generated voices?

Yes, Speakatoo's AI voices are designed to convey human emotions, empathy, and sympathy. This enhances the overall listening experience and makes the generated speech more natural and engaging.

Can I download the generated audio files?

Absolutely! Speakatoo allows users to download text to speech Converted files in popular formats such as in mp3, mp4, wav, ogg & flac. This enables easy integration of the audio into various applications or platforms.

How Speakatoo is different from other platform?

Speakatoo is the most popular AI based Text to Speech conversion Platform which is well known for its quality experience in terms of Product Standards as well as best Customer Support. At Speakatoo, you get 100% Real Human Voiceover experience.

Can I integrate Speakatoo TTS with my own applications or websites?

Yes, Speakatoo TTS supports REST API integration through which you can integrate our services with any third-party applications or websites. Please refer our documentation for more reference.

Is Speakatoo's Urdu text to speech suitable for professional use?

Certainly! Speakatoo's text to speech Urdu is an excellent choice for various professional applications. It can be used in Social media platforms, e-learning platforms, voice-over projects, automated customer support systems, and much more.

Additional Text To Speech Voices

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Urdu (Pakistan) Text to Speech Converter

Text-to-speech urdu (pakistan) by ttsconverter.io. online speech synthesis with natural sounds, and human-like voices. free mp3 download., add background music.

We connect with FreeMusicBG - A collection of free music for commercial or free use, with an attribution license to the author. You can view more and find the Track's ID here: https://freemusicbg.com

• https://www.youtube.com/watch?v=VIDEO_ID
• https://www.youtube.com/watch?v=VIDEO_ID&feature=youtu.be
• https://youtu.be/VIDEO_ID
• https://www.youtube.com/embed/VIDEO_ID
• https://www.youtube.com/watch?v=VIDEO_ID&list=PLAYLIST_ID
• https://www.youtube-nocookie.com/embed/VIDEO_ID
• https://soundcloud.com/username/trackname
• https://soundcloud.com/keysofmoon/infinitely-ambient-music-free-download

Background music

How to convert text into speech with urdu (pakistan) accent.

• Type some text or paste your content
• Select language and choose your favorite Urdu (Pakistan) voice to convert text to speech. Change voice speed and pitch, your way.
• Click the blue " Convert Now " button to start converting
• Play and Download MP3

Urdu PK - Text to Speech voices demo

Text-to-speech Urdu (Pakistan) additional regional language versions

To see more other regional Urdu (Pakistan) text-to-speech, see the pages below:

TTSConverter.io stands out as an innovative platform for Text-to-Speech conversion, leveraging cutting-edge AI advancements. With a diverse selection of more than 700 AI voices, including remarkably lifelike options, it caters to over 140 languages across the globe.

This versatile tool not only serves professional purposes but also empowers you to craft captivating videos tailored for platforms like Facebook, YouTube, Vimeo, Instagram, or personal websites.

Our Free TTS service harnesses the prowess of artificial intelligence and machine learning, drawing from pioneering technologies by tech giants like Google and Microsoft. This synergy allows us to redefine Text-to-Speech by offering a remarkably human-like experience.

Moreover, you have the freedom to fine-tune aspects such as sound characteristics, voice pace, pitch modulation, volume levels, strategic pauses, emphasis incorporation, audio formats, and customized audio profiles.

Text to speech Urdu (Pakistan) Usecases

TTSConverter.io allows you to redistribute your created audio files for free or commercial purposes, no license required.

All intellectual rights belong to you.

Voice over for videos

Podcast - Broadcasting

E-learning material

Sales & Social media

Call Centers & IVR System

Besides, You can use TTSConverter.io to quickly make text-to-speech Urdu (Pakistan) videos and audio files for different purposes without needing a license. You can also see what people usually do with Urdu (Pakistan) accents through some of these suggestions:

• Top best Urdu (Pakistan) text to speech voices 2024
• Free Urdu (Pakistan) Text to Speech 2024
• Urdu (Pakistan) Text to Speech
• Text to speech online Urdu (Pakistan) videos
• Urdu (Pakistan) text to speech audiobooks
• Urdu (Pakistan) voice over
• Urdu (Pakistan) voice AI
• TTS Urdu (Pakistan) YouTube videos
• Urdu (Pakistan) text to speech TikTok videos
• Urdu (Pakistan) TTS social media stories
• Urdu (Pakistan) text to speech software audio messages

Frequently Asked Questions about Urdu (Pakistan) Text-to-Speech (TTS)

Below are some common questions and answers. If you can't find your answer, please email us at [email protected] , we will reply you soon.

What is Text-to-Speech conversion?

How can i convert text to speech, what are the best text-to-speech services available now, why do i need to convert text to speech, can i use text-to-speech conversion services for free, can i download the audio after converting text to sound.

Urdu Text To Speech | Urdu Text to Speech Software Download

Looking for a way to Download  Urdu Voice Typing, Speech to Text for Windows 10/8/7 PC ? You are in the correct place then. Keep reading this article to get to know how you can Download and Install one of the best Tools App  Urdu Voice Typing, Speech to Text for PC.

Most of the apps available on Google play store or iOS Appstore are made exclusively for mobile platforms. But do you know you can still use any of your favorite Android or iOS apps on your laptop even if the official version for PC platform not available? Yes, they do exits a few simple tricks you can use to install Android apps on Windows machine and use them as you use on Android smartphones.

About this app

On this page you can download Urdu Text To Speech and install on Windows PC. Urdu Text To Speech is free Tools app, developed by LucidSWs Inc.. Latest version of Urdu Text To Speech is 17.0.0, was released on 2022-12-09 (updated on 2022-12-29). Estimated number of the downloads is more than 1,000. Overall rating of Urdu Text To Speech is 2,7. Generally most of the top apps on Android Store have rating of 4+. This app had been rated by 52 users, 29 users had rated it 5*, 20 users had rated it 1*.

How to install Urdu Text To Speech on Windows?

Instruction on how to install  Urdu Text To Speech  on Windows 7/8/10/11 Pc & Laptop

In this post, I am going to show you how to install  Urdu Text To Speech on Windows PC by using Android App Player such as BlueStacks, LDPlayer, Nox, KOPlayer, …

Before you start, you will need to download the APK/XAPK installer file, you can find download button on top of this page. Save it to easy-to-find location.

[Note]  You can also download older versions of this app on bottom of this page.

Below you will find a detailed step-by-step guide, but I want to give you a fast overview of how it works. All you need is an emulator that will emulate an Android device on your Windows PC and then you can install applications and use it – you see you’re playing it on Android, but this runs not on a smartphone or tablet, it runs on a PC.

Installation instructions

Urdu Text To Speech works on any Android device (requires Android 5.0 or later). You can also install and run this application on your computer by using an Android emulator app. Here’s how to do it:

How to install Urdu Text To Speech on Android devices

Android devices have the ability to “sideload” applications. Here’s how you can do it.

Step 1: Setting up your device

From your smartphone or tablet running Android 4.0 or higher, go to Settings, scroll down to Security, and select Unknown sources. Selecting this option will allow you to install apps outside of the Google Play store. Depending on your device, you can also choose to be warned before installing harmful apps. This can be enabled by selecting the Verify apps option in the Security settings.

On devices running an earlier version of Android, go to Settings, open the Applications option, select Unknown sources, and click OK on the popup alert.

Step 2: Downloading Urdu Text To Speech apk

The next step will be downloading  Urdu Text To Speech installer file , also known as an APK, which is the way Android apps are distributed and installed.  Urdu Text To Speech apk  downloaded from ChipApk is 100% safe and virus free, no extra costs.

Download App Click Here

Step 3: The process

You can either download the APK file on your mobile device or on your computer, although the latter is a little more difficult. To get started, download an APK file using either Google Chrome or the stock Android browser. Next, go to your app drawer and click Downloads; here you will find the file you just downloaded. Open the file and install the app.

If you downloaded the APK file on your computer, the process is slightly different. You must connect your Android device to the PC and enable USB mass-storage mode. The next step is to drag and drop the file onto your device. Then, using a file manager, such as Astro or ES File Explorer, you can locate the file on your device and install it.

How to install Urdu Text To Speech on your computer (Windows PC, Mac, …)

ou can run Android apps on your computer using an Android emulator app. There’re many Android emulators but in this tutorial we use BlueStacks. It’s available for both Mac and Windows.

Step 1: Setup

Installing BlueStacks is a very simple process. All you have to do is download the program from the  BlueStacks website  and run the file. The installer file is quite large and the engine setup may take awhile.

Once the initial installation process is done, opening the program doesn’t take more than a few seconds. when it opens, you will be asked to sign in using a Google account like any Android smartphone or tablet.

During the installation process, you may come across error messages like “Hardware acceleration is not available on your system” or “This host supports Intel VT-x, but it is disabled.” Enabling hardware acceleration features help virtualization apps run smoother and much faster — apps like Bluestacks are basically running an entire OS on top of your current system.

Download Software Click Here

Step 2: Installing Urdu Text To Speech APK

If you haven’t installed any other programs that associate with the APK file type, BlueStacks will automatically open APK files. Double-click the file to launch BlueStacks and install the app. You can also drag and drop the APK file onto the BlueStacks home screen. Confirm that you want to install the app, and it will appear on your BlueStacks home screen after installing.

That’s it! Now you have successfully installed Urdu Text To Speech on your computer using Bluestacks.

About Urdu Text To Speech

Copy any Urdu text and listen to it like an audio recording. You can also record it

Copy any Urdu text and listen to it like an audio recording. You can also record the text to an audio (mp3) file without any ambient noise!

With this tiny app, you can listen to Urdu text anywhere in your phone. Copy any text and click the button in your notification bar to listen to it. You can also listen to the text by selecting the text and clicking on Urdu Speech in the popup action menu.

Ways this app can be used: 1: Open this app and write Urdu text and click on the speech button. 2: Open this app and write Urdu text and click on the MIC button to save the Urdu text in an Audio(.mp3) file in a computer-generated voice. 3: Open this app and go back to any app you use and copy any text in there and open notification panel and click on the Speak button so this app can speak the text for you. 4: Open any app and select a text and an option menu should appear saying (Copy, Select All etc.), in that menu, click on the 3 dots(options) button to see “Urdu To Speech” button and click on that.

Urdu Voice Typing, Speech to Text for PC – Conclusion:

Urdu Voice Typing, Speech to Text has got enormous popularity with it’s simple yet effective interface. We have listed down two of the best methods to Install  Urdu Voice Typing, Speech to Text on PC Windows laptop . Both the mentioned emulators are popular to use Apps on PC. You can follow any of these methods to get  Urdu Voice Typing, Speech to Text for Windows 10 PC .

We are concluding this article on  Urdu Voice Typing, Speech to Text Download for PC  with this. If you have any queries or facing any issues while installing Emulators or  Urdu Voice Typing, Speech to Text for Windows , do let us know through comments. We will be glad to help you out!

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Urdu Text To Speech | Urdu Text to Speech on Windows Pc 2024

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• Urdu AI Voice Generator

Speechify Urdu AI Voice Generator uses advanced AI text to speech technology, which allows video creators, podcasters, narrators, gaming developers, business professionals, and more to create lifelike generative Urdu AI voice overs, saving time and money.

Urdu Al Voice Generator is perfect for beginner content creators and pros alike.

کسی چیز کو اسپیچ میں تبدیل کرنے کے لیے ٹائپ یا پیسٹ کریں...

Select Voice

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Choose from 40+ languages

And 100+ high quality Al voices

Create a free account to continue.

• Convert any text into audio
• 50+ premium voices
• Added layer of security for your documents
• Save your files
• Faster listening speeds (1.1x & above)
• No limits or ads

Paste Web Link

Paste a web address link to get the contents of a webpage

• AI Voice Generator – Jan 24

Amazing Urdu AI Voice Generator Examples

Create anything from chatbots & IVR messages to training videos, podcasts, documentaries and more.

What is Speech synthesis

The leafy forest

Booking confirmation details

Documentary

The roar of the Bengal tiger

Eldoria: The final quest.

Directory message

Cosmic Mysteries

Classic Ratatouille

TikTok for Business

The making of olive oil

Hire one, or all our Urdu AI Voice Actors, for no extra cost

Fine tune our Urdu text to speech voices, edit emotion, tone, speed, and more to get exactly what you need.

American English

British English

Natalie 5.0

Lifelike generative Urdu AI voice overs

Speechify Urdu AI Voice Generator offers natural-sounding Urdu voices for a wide range of use cases, including audiobooks, promo videos, explainer videos, e-learning content, podcasts, and so much more. Here are just a few examples of how you can use Speechify Urdu AI Voice Generator.

Realistic Urdu AI voice generator

Urdu AI Voice Over converts any text in natural sounding Urdu and 50 other languages.

Maximize your productivity

Import scripts with one click and everything is perfectly formatted.

Share & Export

Share your project with your team with a share link. Export in multiple formats.

Scale Up Your Urdu Voice Overs with the Best Urdu AI Voice Over Generator

Watch your script come alive with text to speech. Content creators, you’re welcome.

Zero Learning Curve

Speechify Urdu AI Voice Over Generator works right in your browser. With an easy to use interface, anyone can create a voice over in minutes.

When you need the help, access our video library for short tips and tricks.

Create Urdu AI Voice Overs Instantly

Our Urdu AI Voice Generator uses Artificial Intelligence technology to generate your voice overs instantly and you can fine tune the voice to get exactly what you need.

The most natural-sounding Urdu voices

Our Urdu voice actors sound more fluid and human-like than any other TTS AI reader so you can understand and remember more.

How Urdu AI Voice Over Generator works

Using Speechify Urdu Voice Over Generator is a breeze. It takes only a few minutes and you’ll be turning any text into natural-sounding Voice Over audio.

• Type in the Urdu text you’d like to hear spoken
• Select a voice & listening speed
• Press “Generate”

$10B Public Company uses Speechify AI Voice Over for Earnings Call

On Feb 28, 2023, Endeavor (NYSE: EDR) made history by delivering its annual earnings call using an AI voice over from Speechify.

The Boston Consulting Group (BCG) uses Speechify Voice Over to create a video for its Digital Acceleration Campaign, partnering with the UN.

A more sustainable and more inclusive future for all is on the horizon, thanks to recent advances in digital technology.

See Sample Voice Overs

See samples of natural-sounding ai generated voices that sound just like human voices. The use cases are plenty and range from AI political ads, explainer videos, eLearning, podcasts, and even audiobooks you’ve authored and download the audio file in high quality lossless formats.

AI Political Ads

Create AI driven political ads in minutes and get your message out quickly. Even your interns can do this.

Product Launches

Voice overs that are ready for the big stage and the spotlight. Engage the world with beautiful presentations.

Turn any book you’ve written into an audiobook. Dust off those drafts and bring your stories to life. 

Frequently asked questions

Everything to know about AI Voice Generator

Can I try Speechify Urdu AI voice generator for free?

Yes, Speechify Urdu AI Voice Generator offers a free plan, so you can try it before you commit to a premium plan.

What other languages does Speechify AI voice generator support?

Speechify AI Voice Generator supports over 100 languages, including English, German, French, Italian, Spanish, Russian, Portuguese, Arabic, Hindi, Tamil, Chinese, Japanese, Korean, Indonesian, Ukrainian, and more.

What is an AI voice?

An AI voice refers to a synthesized or generated vocal output produced by artificial intelligence algorithms in the form of Urdu AI voice generator characters, simulating human-like speech patterns for various applications such as virtual assistants, narration, and voice overs.

What is an AI Urdu voice generator?

An AI Urdu voice generator, such as Speechify AI voice generator text to speech, is a technology that utilizes artificial intelligence algorithms to convert text into realistic and natural-sounding human-like speech.

Which is the best Urdu AI voice generator?

There are many top quality Urdu AI voice generators in the market these days and the quality of the voices are more or less on the same quality level. So, choosing the best AI voice generator by just the quality of voices is not the best way to go about it. The best way to look at it is by evaluating the company, the feature set, the roadmap, how quickly they are iterating, the pricing, and the customer support. If you look at all these aspects, Speechify is the best Urdu AI voice generator in the market. If you search for “ai voice generator free” you’ll see quite a few options, but Speechify is the leader in this industry.

Is there a free Urdu AI voice generator?

Yes. Speechify is the best free Urdu AI voice generator. Just create an account and begin using our premium AI voice studio for free!

What is the Urdu AI voice generator everyone is using?

The top Fortune 500 companies, small businesses, content creators, and influencers use Speechify AI Voice Studio.

Can I make an AI version of my own voice in Urdu?

Yes you can, with Speechify voice studio you get more than just voice overs, you can clone your own voice and then use your voice for Urdu AI voice overs. Our AI Dubbing & Cloning products integrate seamlessly with our Voiceover product. There is even an AI voice generator Spongebob tool somewhere out there. While Speechify does not provide this functionality, it goes to show you how versatile this technology is.

Is Speechify voice over different from the text to speech reader?

Yes, Speechify Urdu Voice Over is different from Speechify Text to Speech Reader and they’re two different subscriptions. Speechify’s Text to Speech Reader is an app that reads any text aloud in a natural-sounding voice and can be used to read books, articles, PDFs, emails—whatever you’re reading. Voice Over allows users to create high-quality, human-sounding voice overs for their own content, such as podcasts, videos and audiobooks. Voice Over provides a variety of professional voice actors and offers advanced editing and customization features to help users create the perfect voice over for their content.

What’s the ROI of Speechify Urdu AI voice generator?

Urdu Voice actors regularly cost $200+ for just a minute of audio. Speechify can easily save any business $10,000+ per month. The Speechify Voice Over will also improve the quality and impact of your audio content, which leads to better engagement with your target audience and increased revenue opportunities.

How long can I use the free plan?

As long as you want and it has full functionality. This free plan is designed to give users a taste of our product’s capabilities and to help them decide if they want to upgrade to a paid plan. Once you’re ready to start sharing what you’ve built with the outside world and you need to download your work to an mp3, you can upgrade your plan to Voice Over Pro or reach out to our sales team for enterprise pricing.

Do I have commercial rights with Speechify AI voice over?

Yes! Speechify Voice Over is meant for creators, businesses, and anyone who wants to put their content out into the world. With Voice Over you own the audio output and commercial rights in perpetuity to use for your own projects.

Speechify Studio Pricing

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Pricing Plans

Simple way to get started

$0 per month forever

• No Downloads
• AI Voice Over
• Video, Slide, and Image support
• Try all 200+ voices
• All 20+ languages & accents
• Support adding pauses
• 10 minutes of voice generation
• Support adjusting pronunciation
• Support uploading of .txt, .docx, .srt scripts, as well as Youtube URLs

The basics for individuals

$69 per month / user

Everything in Free

• Download as video, audio, or text
• Video and audio Dubbing
• Video and audio Transcription
• 50 hours of voice generation per user/year
• 12 hours of Dubbing per user/year
• 50 hours of Transcription per user/year
• Commercial usage rights
• 8000+ licensed soundtracks
• Thousands of Stock Images & Videos

MOST POPULAR

Professional

For professionals and teams

$99 per month / user

Everything in Basic

• Voice Cloning
• 100 hours of voice generation per user/year
• 36 hours of Dubbing per user/year
• 100 hours Video and Audio Transcription
• 1 hour of AI Avatar Video/year

Customizable capability based on your business needs

Everything in Professional

• Multiple seats
• 1,000+ hours of voice generation per user/year
• 500+ hours of Dubbing per user/year
• 1,000+ hours Video and Audio Transcription
• 20+ hours of AI Avatar Video/year
• White Glove Procurement Assistance
• Dedicated Customer Success Manager
• Share, Editing, Commenting & Enterprise Collaboration Features
• Custom Invoices
• SOC2 Compliant
• Company-wide on-boarding & Training

AI voice generator use cases

Urdu social media marketing.

Create or edit TikToks, Instagram Reels, posts, YouTube videos or YouTube Shorts. No more waiting for a video editor to create your video. Get your message out early and get quick results. Video content creation is a breeze and works right in your browser.

Urdu Audiobook and E-Learning Narration

Urdu AI voice overs are used to narrate audiobooks you’ve authored and e-learning modules, providing a cost-effective and efficient alternative to human narrators. They offer consistent voice quality and can easily adapt to different genres or educational content. Quickly convert text to audio with lifelike custom voices, even your own voice, with our built in voice cloning feature with real-time previews.

Customer Service and Virtual Assistants

In customer service, Urdu AI voice overs power virtual assistants and chatbots, offering a more natural and engaging interaction for users. They can handle routine queries, guide users through processes, and provide information 24/7 without human intervention.

Urdu Video Game and Animation Character Voices

Urdu AI voice overs can bring characters in video games and animated films to life. AI speech generators allow for a wide range of voices and can be easily modified to suit different characters, from fantasy creatures to realistic human characters.

Product Demos

Quickly create product demo videos and publish them to your site or social media. Gone are the days where creating a great demo took time. Now you can get your demo out there in minutes. Our text to speech technology offers high quality voices, even AI avatars to help explain your product features in an engaging manner.

Marketing and Advertising

In the marketing and advertising industry, AI voice overs are used for creating commercials, product videos, and promotional content. They offer flexibility in terms of voice styles and can be tailored to target specific demographics.

Accessibility Features for the Visually Impaired

Urdu AI voice overs play a crucial role in making content accessible to visually impaired individuals. They can be used to read out text from websites, apps, and digital documents, enabling easier navigation and access to information for those with visual impairments.

Automated Customer Service and Call Routing in IVR Systems

Urdu AI voice overs are increasingly employed in IVR systems used by businesses for automated customer service. These systems can handle a high volume of incoming calls, providing callers with clear, natural-sounding voice prompts. Best of all, you can create these messages in minutes!

Multilingual Voice overs

For those looking to scale their multilingual video or audio strategy, easily convert your script to over 50 languages. From Italian, and Japanese, to Hindi, Portuguese, or even Korean. Grow your global audience with human-like voices with inflections and emotion.

Urdu AI Voice Over for Corporate Training Videos

Quickly create a library of training videos that are easily editable. Simply tweak the script and with our speech synthesis engine, you can generate a new training video in minutes. Choose from high quality voices that sound professional, even edit the voice to best match your brand.

Speechify AI Voice Over Generator online reviews

It’s so easy to control and to use with any podcast or project for school.

Incredible!

This is incredible! The quality of the voices you offer is unmatched compared to the other services I’ve been experimenting with.

This application and its features are amazing. I like how the voices sound less robotic, and how efficiently and quickly the voice overs can be edited and generated.

I love that the voice over recognizes punctuation and enunciates with such clarity.

Better than Murf

Way better than Murf! It actually sounds realistic.

Sound natural

Great voice over software overall. lots of customization for emphasis and making it sound more natural. It’s sounding not so robotic and more and more human to me.

Absolutely stunning

This is the best service I’ve used so far! Absolutely stunning.

This is so perfect. This is exactly what I was looking for. It contains all the features. Thank you so much. Truly appreciate it.

Only available on iPhone and iPad

To access our catalog of 100,000+ audiobooks, you need to use an iOS device.

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Translate by speech

If your device has a microphone, you can translate spoken words and phrases. In some languages, you can hear the translation spoken aloud.

Important: If you use an audible screen reader, we recommend you use headphones, as the screen reader voice may interfere with the transcribed speech.

Translate with a microphone

Important: Supported languages vary by browser. You can translate with a microphone in Chrome and there’s limited support in Safari and Edge.

• On a Mac: Microphone settings are in the System Preferences .
• On a PC: Microphone settings are in the Control Panel .

• On your computer, go to  Google Translate .
• Translation with a microphone won’t automatically detect your language.

• Speak the word or phrase you want to translate.

Listen to translations spoken aloud

• Go to Google Translate .
• Choose the languages to translate to and from.
• In the text box, enter content you want to translate.

Troubleshoot error messages

Need permission to use microphone, voice input isn't supported on this browser, voice input isn't available, we're having trouble hearing you.

If you get an error message that says "We're having trouble hearing you," try these steps:

• Move to a quiet room.
• Use an external microphone.
• Turn up the input volume on your microphone.

Related resources

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Translate a bilingual conversation

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اُردو تقاریر اور مضامین | urdu speeches & essays in written form, urdu speech hub best urdu content on internet.

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Use Text-to-speech in Urdu

I am using Windows 10 pro. I am trying to add TTS for reading urdu pdf file. Please let me know how I can add this feature. 

***Modified title from: TTS - Urdu***

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• Microsoft Agent |

Thank you for writing to Microsoft Community Forums.

I understand that you want to use Text-to-speech for reading an Urdu PDF file.

I would like to inform you that currently, the Text-to-speech feature is unavailable in Urdu language. To know about the supported languages, you can refer the section Standard voices from the article Language and region support for the Speech service .

However, your suggestions and feedbacks are always appreciated. We do have a place, where you can share the suggestions and feedbacks, I would suggest you to share your suggestions and feedbacks in the Feedback Hub , where our developers can listen to your voice and constantly improve the features.

Nikhar Khare

19 people found this reply helpful

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Speech emotion recognition for the Urdu language

Dataset and evaluation

• Original Paper
• Published: 13 August 2022
• Volume 57 , pages 915–944, ( 2023 )

Cite this article

• Nimra Zaheer   ORCID: orcid.org/0000-0002-7074-2988 1 ,
• Obaid Ullah Ahmad 1 ,
• Mudassir Shabbir 2 &
• Agha Ali Raza 3  

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Crafting reliable Speech Emotion Recognition systems is an arduous task that inevitably requires large amounts of data for training purposes. Such voluminous datasets are currently obtainable in only a few languages, including English, German, and Italian. In this work, we present SEMOUR \(^+\) : a S cripted EMO tional Speech Repository for Ur du, the first scripted database of emotion-tagged and diverse-accent speech in the Urdu language, to design an Urdu Speech Emotion Recognition system. Our gender-balanced 14-h repository contains 27, 640 unique instances recorded by 24 native speakers eliciting a syntactically complex script. The dataset is phonetically balanced, and reliably exhibits varied emotions, as marked by the high agreement scores among human raters in experiments. We also provide various baseline speech emotion prediction scores on SEMOUR \(^+\) , which could be utilized for multiple applications like personalized robot assistants, diagnosis of psychological disorders, getting feedback from a low-tech-enabled population, etc. In a speaker-independent experimental setting, our ensemble model accurately predicts an emotion with a state-of-the-art \(56\%\) accuracy.

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Acknowledgements

This work is partially supported by the Higher Education Commission (HEC), Pakistan under the National Center for Big Data and Cloud Computing funding for the Crime Investigation and Prevention Lab (CIPL) project at Information Technology University, Lahore. We acknowledge the efforts of our volunteers including Sidra Shuja, Abbas Ahmad Khan, Abdullah Rao, Talha Riaz, Shawaiz Butt, Fatima Sultan, Naheed Bashir, Farrah Zaheer, Deborah Eric, Maryam Zaheer, Abdullah Zaheer, Anwar Said, Farooq Zaman, Fareed Ud Din Munawwar, Muhammad Junaid Ahmad, Taha Chohan, Sufyan Khalid, Iqra Safdar, Anum Zahid, Hajra Waheed, Mehvish Ghafoor, Sehrish Iqbal, Akhtar Munir, Hassaan, Hamza, Javed Iqbal, Syed Javed, Noman Khan, Mahr Muhammad Shaaf Abdullah, Talha, Tazeen Bokhari and Muhammad Usama Irfan. We also thank the staff at ITU FM Radio 90.4 for their help in the recording process.

Funding was provided by Higher Education Commission, Pakistan.

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Zaheer, N., Ahmad, O.U., Shabbir, M. et al. Speech emotion recognition for the Urdu language. Lang Resources & Evaluation 57 , 915–944 (2023). https://doi.org/10.1007/s10579-022-09610-7

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An Urdu speech corpus for emotion recognition

Awais asghar.

1 Sino-Pak Center for Artificial Intelligence, Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology, Haripur, Pakistan

2 Department of Electrical Engineering, University of Engineering and Technology, Taxila, Punjab, Pakistan

Sarmad Sohaib

3 Department of Electrical and Electronic Engineering, University of Jeddah, Jeddah, Saudi Arabia

Saman Iftikhar

4 Faculty of Computer Studies, Arab Open University, Riyadh, Saudi Arabia

5 Department of Computing, School of Electrical Engineering and Computer Science, National University of Science and Technology, Islamabad, Pakistan

Muhammad Shafi

6 Faculty of Computing and Information Technology, Sohar University, Sohar, Oman

Kiran Fatima

7 TAFE, New South Wales, Australia

Associated Data

The following information was supplied regarding data availability:

The raw data are available in the Supplemental Files .

Emotion recognition from acoustic signals plays a vital role in the field of audio and speech processing. Speech interfaces offer humans an informal and comfortable means to communicate with machines. Emotion recognition from speech signals has a variety of applications in the area of human computer interaction (HCI) and human behavior analysis. In this work, we develop the first emotional speech database of the Urdu language. We also develop the system to classify five different emotions: sadness, happiness, neutral, disgust, and anger using different machine learning algorithms. The Mel Frequency Cepstrum Coefficient (MFCC), Linear Prediction Coefficient (LPC), energy, spectral flux, spectral centroid, spectral roll-off, and zero-crossing were used as speech descriptors. The classification tests were performed on the emotional speech corpus collected from 20 different subjects. To evaluate the quality of speech emotions, subjective listing tests were conducted. The recognition of correctly classified emotions in the complete Urdu emotional speech corpus was 66.5% with K-nearest neighbors. It was found that the disgust emotion has a lower recognition rate as compared to the other emotions. Removing the disgust emotion significantly improves the performance of the classifier to 76.5%.

Introduction

Emotion recognition is a vital aspect towards complete human-machine interaction since effective communications of information is fundamental to human-machine interaction. Emotion recognition is also a vital part of automatic human behavior analysis such as assessing candidates’ suitability for a job, assessing emotional intelligence, and lie detection, etc . There are many ways in which machines can recognize emotions such as face recognition, gestures, eye movements, body language, and electrocardiogram (ECG) signals ( Soleymani et al., 2016 ). Among all these, speech is an easy and effective form of interaction. Hence, the literature in emotion detection research is focused on the interpretation of emotions from human speech ( Dahake, Shaw & Malathi, 2016 ). There are several applications of emotional understanding such as E-learning where the tutor can change the presentation style when a learner is feeling uninterested, angry, or interested. Similarly, in medical sciences, virtual assessment of the patients’ health is possible by listening to his/her voice. In the robot-human communication, the robots can be trained to communicate with human-based emotional states. The cellular services, multimedia devices and call centers have vast area of application related to emotion recognition where devices can detect the human behavior (frustration and annoyance etc.) of end user and react accordingly. Usually, the emotion recognition from the speech is performed by collecting datasets (training, testing and validation), performing statistical analysis (extraction of the features that are associated to the different emotional states), and to classify the emotions from the acoustic signals, as illustrated in Fig. 1 ( Yadav & Aggarwal, 2015 ).

Extensive literature is available in the field of human emotions recognition for different languages such as English, French, German, and Malayalam in the last few years ( Ververidis & Kotropoulos, 2003 ). For these languages, the developed emotional speech datasets comprises the collection, careful annotation, noise filtering, and validation of speech samples. However, such databases need to be developed for other global languages. The Urdu language has more than 11 million speakers worldwide as a native language and 105 million second-language speakers in the world ( BBC, 2022 ). However, speech emotion recognition (SER) from the Urdu language needs further research ( Qasim et al., 2016 ; Kaminska, Sapinski & Anbarjafari, 2017 ) and significant improvements such as noise filtering, careful annotation and validation of samples in the development of the Urdu language emotional dataset. Owing to this lack of consideration in Urdu language dataset collection, Urdu emotional speech database with noise filtering, careful annotation, and sample validation features is realized in this study. The emotion recognition performance is predominantly affected by the pre-processing, feature extraction, and algorithms used to classify the speech into various emotions. In this study, K-nearest neighbour (k-NN), Random Forest (RF), and multiclass Support Vector Machine (SVM) with the linear kernel are used to validate the efficiency of the feature sets.

The remainder of this article is organized as follows. “Background and Related Work” describes the related work and background of research. “Dataset Collection” provides an overview of Urdu emotional speech corpus collection, assignments of labels, and Urdu utterances selected for the recording. “Pre-Processing” explores the pre-processing. “Feature Extraction” provides details of feature extraction, and ML algorithms. “Results and Discussions” presents the classification results. Finally, “Conclusion” concludes the paper with future directions.

Background and Related Work

In the field of natural language processing (NLP) and automatic speech recognition (ASR), several speech corpora have been developed for various languages ( Douglas-Cowie et al., 2003 ; Dimitrios Ververidis, 2019 ). Many successful proposals have been proposed in the emotion classification for resource rich languages such as Italian ( Giovannella et al., 2009 ), Polish ( Staroniewicz & Majewski, 2009 ), German ( Grimm, Kroschel & Narayanan, 2008 ), English ( Livingstone & Russo, 2018 ), and French ( Gournay, Lahaie & Lefebvre, 2018 ). However, emotion recognition in the Urdu language is still a target research area and there is a sufficient opportunity for the improvement. Due to the insufficiency of the emotion recognition techniques for the Urdu language, emotion recognition systems for other languages are summarised below, followed by such systems for the Urdu language.

Livingstone & Russo (2018) and Zhang, Provost & Essi (2016) presented a multimodal English language emotional speech and song corpus in Livingstone & Russo (2018) , Zhang, Provost & Essi (2016) . The dataset is collected from 24 professional actors by simulating two neutral statements, that is, “Dogs are sitting behind the door” and “Kids are talking by the door”. Seven emotions are selected for the speech whereas five for the song, respectively. Every emotion is simulated with two levels of intensity that is strong and neutral. To validate the dataset, 247 untrained individual opinions are taken on each emotion. Kaminska, Sapinski & Anbarjafari (2017) developed an emotion recognition framework for the Polish language, where the dataset is recorded in two different forms of emotional speech that is spontaneous and acted speech. Spontaneous speech samples are collected from live TV shows and programs such as news and reality shows. The acted speech samples are recorded from eight native speakers of both genders (four males and four females) where they uttered 240 sentences in six different emotions. The validation of the dataset is endorsed by the subjective listening test. An accuracy of 72% is achieved in emotion recognition. Statistical analysis is also performed to validate the corpus . A pool of the features including Perceptual Linear Prediction (PLP), Bark Frequency Cepstral Coefficient (BFCC), and Human Factor Cepstral Coefficients (HFCC) is used to classify the emotions. The achieved accuracy of this experiment for natural and acted speech is 81% and 60% respectively. Lyakso et al. (2015) developed the first emotional speech corpus of children in the Russian language and named as the EmoChildRu. It was comprised of audio samples of 120 children simulated in three different emotions including the comfort, discomfort, and neutral. The basic emotions of anger, sadness, and fear are expressed as discomfort. Leila et al. (2019) achieved an accuracy of 83% in recognition of seven basic emotions on the German EmoDB database after applying feature selection and speaker normalization techniques. The Mel Frequency Capstrum Cofficient (MFCC) and Modulation Spectral Features (MSFs) methods were used for feature extraction. Kumar & Iqbal (2019) and Khalil et al. (2019) discussed different classifiers such as k-NN, SVM, convolutional neural networks (CNN), recurrent neural networks (RNN), and long short-term memory (LSTM) and some feature extraction techniques in Kumar & Iqbal (2019) , Khalil et al. (2019) and Zhao, Mao & Chen (2019) , respectively. Pengcheng & Zhao (2019) proposed an emotion recognition system for the Chinese language, where denoising auto-encoder and sparse autoencoder are used for feature extraction whereas the wavelet kernel sparse SVM classifier is used for the classification. Tripathi & Beigi (2018) have used RNN with three hidden layers to recognize emotion for the IEMOCAP database with an accuracy of 71.04%. This study used only four emotions that is happiness, sadness, neutral, and anger. Tang, Zeng & Li (2018) recognized seven basic emotions from the corpus named as emotional sensitivity assistance system for people with disabilities (EmotAsS) ( Simone et al., 2017 ) and achieved an accuracy of 45.12% with RNN, CNN and ResNet. Sarma et al. (2018) and Eskimez, Duan & Heinzelman (2018) used the IEMOCAP dataset for sentiments recognition, where classification is carried out using the LSTM and CNN. An accuracy of 70.06% and 47% is achieved for LSTM and CNN, respectively. Latif et al. (2018) presented a cross-lingual recognition system: Urdu vs Western language. A recognition accuracy of 83.04% was achieved for the Urdu dataset when other languages are used in training set on four basic emotions. SVM, logistics regression, and random forest are used for classification. Panagiotis et al. (2017) proposed a system with RNN and ResNet that gives recognition rates of 78.7% on the French language based remote collaborative and affective (RECOLA) dataset. The details of the RECOLA are explained by Fabien et al. (2013) . Mao et al. (2017) introduced an Emotion-discriminative and Domain-invariant Feature Learning Method (EDFLM) in Mao et al. (2017) . It provided a good emotion recognition rate on the INTERSPEECH 2009 challenge and the Emo-DB database. Fayek, Lech & Cavedon (2017) and Mirsamadi, Barsoum & Zhang (2017) both use the IEMOCAP dataset with RNN and CNN obtained 64.78% and 63.5% of accuracy, respectively. Mirsamadi, Barsoum & Zhang (2017) used both Low-Level Descriptors (LLDs) and High-Level Statistical Functions (HSFs) as input to SVM in order to differentiate emotions. Rajisha, Sunija & Riyas (2016) performed analysis on the Malayalam language to differentiate different sentiments. MFCC, energy, and pitch are used for features extraction. The four basic emotions (happiness, sadness, neutral, and anger) are classified by SVM and artificial neural network (ANN). Yadav & Aggarwal (2015) achieved an 85% accuracy to recognize four emotions with ANN. Sinith et al. (2015) tested the SVM with two classification strategies that is one against one, and one against all in Sinith et al. (2015) . The SVM gives a higher performance on Berlin emotional database as compared to Malayalam emotional database with a feature set of MFCC, energy, and pitch. Abbas, Khan & Bashir (2015) performed a classification of emotions for Urdu language ( Abbas, Khan & Bashir, 2015 ) where J48 and Decision tree are tested, achieving an accuracy of 48% with four basic emotions. Fayek, Lech & Cavedon (2015) achieved an emotions recognition rate on eINTERFACE and SAVEE database in Fayek, Lech & Cavedon (2015) which was 60.53% and 59.7%, respectively. The Polish language emotion speech dataset obtained 70% accuracy with k-NN.

Table 1 presents a summary of the emotion recognition techniques from the literature. Rauf et al. (2015) proposed a speaker-independent Urdu language speech recognition system where the dataset comprises the utterances for district names of Pakistan. A total of 139 district names are recognized in major Urdu language accents such as Punjabi, Sindhi, Balouchi, and Pashto. Ali et al. (2013) presented an Emotions-Pak corpus , where only one utterance “In seven hours it will happen” is recorded in Urdu and other provincial languages of Pakistan. In this corpus , four emotions are obtained in a given sentence. To evaluate the performance of recorded emotions, results from the prosodic feature set and subjective listening were compared. Andleeb, Haider & Abbas (2017) performed the classification of the special and normal children’s speech emotions in Urdu language. A total of 11 different feature extraction techniques including MFCC, Linear Prediction Coefficient (LPC), and PLP are used to classify the special and normal children’s speech. The dataset was recorded using 200 special and 200 normal children in four different emotions on the selected utterance “I have to play” in Urdu. Abbas, Zehra & Arif (2013) presented a system that recognized the emotions in the provisional languages of Pakistan, where only one utterance was simulated in Pakistani languages for four basic emotions. The achieved accuracy was 75% where Multi-layer Perceptron (MLP), and Naive Bayes were used as classifiers.

Dataset Collection

Our emotional speech corpus comprises 2,500 emotion samples of Urdu speech. There are 20 speakers of both genders (10 males and 10 females) aging between 20 to 40 years. Each speaker utters five times. Every time a speaker utters five different Urdu utterances in five different emotions such as happy, sad, angry, disgust, and neutral. The selected utterances are everyday human-human interaction utterances and easy to understand in all five emotions. The utterances were recorded in the university lab using the Blue Yeti desktop microphone as recording equipment. After collection, the recorded emotional speech utterances were listened by a psychologist and a group of students (10–15) to verify the originality of simulated emotions. The speech utterances which were repeatedly mismatched with the assigned labels were discarded from the emotional corpus . A large number of samples were discarded from the disgust emotion which was also highlighted in the Results and Discussion sections. For this reason, the samples per emotion were not balance. The fully filtered emotional speech dataset was then fed to the emotion recognition system. The complete process of the emotional speech dataset is outlined in Fig. 2 .

Description of audio speech clips

The Urdu emotional speech dataset contains a total of 2,500 audio clips that was simulated by 20 speakers of both genders. Each speaker uttered 125 emotional speech clips that include five emotional states that were angry, happy, neutral, disgust, and sad on five commonly used Urdu language utterances. The full constructed data recording includes the number of clips per speaker = angry (5) × utterances (5) × repetition (5) = 125; for 20 speakers, the total number of audio clips became 125 × 20 = 2,500. In the validation stage, 200 samples, which were not correctly uttered, were filtered out. The distribution of remaining 2,300 audio clips/emotional speech samples is provided in Table 2 .

Recording environment

The utterances were recorded in a noise-free lab room in absence of the background noise to achieve good quality. The speakers were asked to sit in front of a microphone, and they may move their bodies freely to express a particular emotion. Further, the speakers were asked to speak in the direction of a microphone to capture the full intensity of voice. The distance between the subject and recording equipment is kept at 25 cm.

Acted or real emotion

A fully developed emotion appears occasionally in the real-life. From the real-life speech samples, it is almost impossible to differentiate between some basic emotions ( Burkhardt et al., 2005 ). Hence the literature prefers the acted emotions. There are a few factors to be considered while collecting acted speech. (I): All speakers should act the same verbal content in order to allow the comparability across emotions and speakers. (II): The quality of the recorded voice assumed to be good enough, minimizing background noise; otherwise spectral measurements would not be possible. (III) a reasonable number of speakers should perform all emotions to obtain generalization over the target emotions.

Choice of emotions and speakers

To compare the selection of emotions with early research ( Yadav & Aggarwal, 2015 ; Giovannella et al., 2009 ; Grimm, Kroschel & Narayanan, 2008 ), the same emotions were used, such as: happy, sad, angry, disgust, and neutral. These emotions attract more attention and used in the human daily life. These selected emotions are easy to understand by the speakers as well as the listeners. It is important to note that we have not involved trained actors in performing emotional expression. All the speakers were students and faculty members of the department. However, the speakers were aware and trained before the actual recording of the emotions.

Text material

The utterances used were easy to understand in the emotions, that is, there were no emotional biases involved. The literature suggested two types of text materials that can ensure such requirements ( Costantini et al., 2014 ), (I): the text material that was emotionally neutral, and (II): normal sentences which are used in everyday life. In the preparation of the database, priority was given to the neutrality of speech material, and thus everyday sentences were used as test utterances. Five sentences were chosen which could easily be interpretable in the above-mentioned emotions. These sentences are given in Table 3 .

Recording of data

There was only one session of recording per day with three speakers. All the recordings were completed under the supervision of psychologist and experts, and their opinions on the emotion were also recorded. The collected speech samples were normalized and stored in “.wav” format with sampling frequency 44.1 kHz, and 16 bits per sample. A Blue Yeti desktop microphone was used to record the speech samples. The utterances were recorded in a noise-free lab room in absence of the background noise to achieve the good quality ( Gournay, Lahaie & Lefebvre, 2018 ).

Database validation

Based on the opinions of experts and psychologists during the collection stage, the utterances were extracted and initially classified into one of the five discrete emotion categories including happiness, sadness, anger, disgust, and neutral state. A psychologist was asked to listen carefully the randomly presented audio files and indicate which of the emotion is available in the presented files. The psychologist was not allowed to go back to previously presented emotion. Another labelling exercise was carried out where 10 to 15 students were included in the tests. Every student was presented with the acted emotions (.wav audio files) to make a decision about the simulated emotions and check the performance of speakers. Therefore, the speech samples which repeatedly mismatched with the labels were discarded from the emotional corpus . The fully filtered emotional speech dataset was then fed to the developed emotion recognition system. The recognition rate of each emotion is shown in Table 4 .

Pre-Processing

In the emotion recognition system, there can be silence parts and background noise in the spoken utterances. Therefore, the emotional speech signals recordings from the microphone are first pre-processed and made them suitable and noise-free for feature extraction stage. In this study, silence parts and background noise are removed manually. Figure 3 demonstrates the pre-processing steps which are discussed in the subsections.

Pre-emphasis

The high-frequencies were suppressed during the sound production by humans. Therefore, pre-emphasis was applied on the sampled signal to increase the magnitude of higher frequencies, thereby improving the overall signal-to-noise ratio (SNR). The pre-emphasis was implemented as a first order Finite Impulse Response (FIR) filter which is defined as:

where y ( n ) is the emphasized signal, x ( n ) is sampled signal and a is the pre-emphasis coefficient, with value raging from 0.9 to 1.0.

Speech signal is non-stationary by nature and the spectral analysis usually considers the stationary signals. Therefore, framing was used to convert the non-stationary speech signals into stationary signals. During the framing, the speech signal was divided into a series of the overlapping frames. The frame length was 20 to 30 ms with an overlap of 1 / 3 of the frame size. Overlapping was used to avoid loss of data due to aliasing.

Hamming window

The sudden change at the onset and offset of frame causes loss of important information. Therefore, Hamming windowing function was applied to all frames. If w(n) is the Hamming window function and y(n) is the input signal frame, then output z(n) is given by equation as:

N is number of samples in a frame and z(n) is a final pre-processed signal.

Feature Extraction

After all pre-processing, the signal is appropriate for feature extraction. Various statistical values were used in our model to discriminate emotion classes. These statistical values are in the form of vectors known as feature vectors. These feature vectors provide a higher level of representations of audio samples. The extracted features in this study are explained below.

Spectral flux

It is a one-dimensional feature vector against one audio sample. It is a measure of how rapidly the power spectrum of a speech signal varies and is calculated by comparing the power spectrum of two successive frames and computed as the squared difference between the standardized magnitudes of spectra of two consecutive short-term windows and is given by Alías, Socoró & Sevillano (2016)

It is also known as the Euclidean distance among the two standardized spectra.

Spectral centroid

The spectral centroid shows where the centre of gravity of the spectrum of the audio signal is located ( Kamarudin et al., 2014 ). It is obtained by taking a weighted average of the frequency components present in the signal. The weighted average is determined by taking Fourier transform of frequencies and their magnitude as weights and calculated as:

where Z t ( n ) is the magnitude of Fourier transform at frame t and frequency bin n .

Spectral roll off

Spectral roll-off is a feature that is defined as the frequency under which 85% of the signal’s spectral energy is accumulated. This measurement gives the centre of mass of energy (higher frequencies) in the spectrum ( Kaur & Kumar, 2017 ).

Zero crossing

Zero crossing is a method to classify the voice and non-voice parts of the signal. It is the rate at which speech signals passes through zero level ( Toledo-Pérez, Rodríguez-Reséndiz & Gómez-Loenzo, 2020 ). Zero crossing for the signal can be calculated as

Energy is a very basic and fundamental feature in signal processing ( Li & Sun, 2008 ). Energy of speech signal is referred to an intensity of a signal and is calculated as

For example, energy of the happy and angry is different from sad and neutral.

Linear prediction coefficient

The LPC model describes the vocal tract of the humans. In LPC, each sample of the speech signal is expressed as a linear combination of the earlier samples. These coefficients are highly effective representation of the speech signal ( Alim & Rashid, 2018 ; Dave, 2013 ). In this analysis, each speech sample is represented by a weighted sum of past speech samples plus an appropriate excitation. The corresponding expression for the LPC model is given as:

where p is the order of LPC, a ( k ) is the k th coefficient of LPC vector, z ( n − k ) is the n t h speech sample and e ( n ) is the prediction error. The coefficients a ( k ) are computed by minimizing the sum of squared differences between the actual speech samples and the linearly predicted ones.

Mel frequency capstrum coefficient

MFCC are the commonly used features in speech recognition systems. It is a short-term power spectrum of an audio signal, which is based on the inverse fast Fourier transform (IFFT) of a log power spectrum on a nonlinear Mel scale of frequency. The Mel scale is a perceived pitch or frequency that is heard by the listener to be equal in distance from one another. Human ear can easily understand the difference between pitch changes at low frequency as compared to high frequency. The incorporation of this scale makes our feature vector more closely related to the human hearing system ( Alim & Rashid, 2018 ; Dave, 2013 ). Mel scale frequency can be expressed as:

where f is a linear frequency and f m e l is perceived frequency of speech signal. To move back to linear frequency scale from Mel scale perceived frequency we use

MFCC is implemented using the following steps.

• Segmented the time-domain speech signal.
• For each segment, the periodogram estimate of discrete Fourier transformed (DFT) segments is calculated.
• Applied the Mel scale filter bank on power spectrum, and sum-up the energy for each filter bank.
• Take the log of Mel scaled energies.
• Applied the discrete cosine transform (DCT) on a log Mel scaled energies.
• Keep the first 13 DCT coefficients.

For one audio sample, the total feature vector size is 1 × 64 as summarized in the Table 5 .

Results and Discussions

There are five main blocks in a speech emotion recognition system, that is, emotional speech input, pre-processing, feature extraction, assignment of labels, and classification of the emotions. The complete emotion recognition system is demonstrated in Fig. 4 . After feature extraction, each speech sample results in statistical values against every emotion: angry, happy, sad, neutral, and disgust. Each emotion in a speech sample has a unique intensity, pitch, zero-crossing rate, and spectral feature. It is important to classify the emotions from the aforementioned feature vectors.

In this study, we have used three classifiers, that is, SVM, k-NN, and RF to train and test our Urdu speech emotional dataset. The multi-class problem in the SVM is also solved by using one-against-one and one-against-all SVM strategies ( Hassan & Damper, 2010 ). These heuristic methods are used to split a multi-class classification problem into multiple binary classification datasets and train a binary classification model on each. The performance of one-against-rest SVM is measured as an average of all binary classifier accuracies. The Urdu speech database is divided into two sets, the training and testing sets, where the training set contains 70% and the testing set contains 30% of the whole dataset. Both sets (training and testing) carry information of each speaker’s emotion. During the model training, feature vectors of the training set along with their labels were given to the classifier whereas in testing, the feature vector of the unclassified sample is given to the model. The performance of classifiers was measured on the test data using accuracy, precision, and recall measures.

Finally, the performance of each classifier was compared for each emotion. Our Urdu speech dataset contains five utterances that are simulated in five different emotions i.e. , happy, sad, angry, neutral, and disgust. It was observed that ‘disgust’ is difficult to recognize as compared to the others. It had adverse effects on classification accuracy, while the physiologist also struggled to recognize the disgust emotion. Thus, we divided our data set into two subsets, one with disgust and another without disgust emotion. The classification was implemented in six different ways i.e. , females, males, and a complete dataset is subdivided into with and without disgust emotion. In the classification, the emotions angry are labeled as “A”, disgust as “D”, happy as “H”, neutral as “N”, and sad as “S”.

Table 6 shows the classifiers performance summary with disgust emotion where it can be seen that the k-NN performs better for male and complete datasets. One- vs -rest classifier performance is better in the case of the female dataset. Table 7 shows the classifiers performance without disgust emotion dataset. It can be observed that the k-NN performs the best for the male and complete dataset here too, whereas onevs- rest classifier performs better in the case of the female dataset in this scenario. The comparison with state-of-the art from literature is presented in Table 8 . It is worthwhile to mention here that although one of the benchmarked studies has reported slightly higher accuracy, our work’s scope is wide in terms of the number of emotions (with five emotions as compared to four emotions) and the size of the dataset (2,500 samples as compared to 400 samples). The receiver operating characteristic (ROC) curve differentiates between the true positive rate or truly classified samples in opposition to the false positive rate or not truly classified samples. A good classification technique has an upside-down “L” shape curve while others follow diagonals. Figures 5 and ​ and6 6 show the ROC and area under the curve (AUC) for every emotional state i.e . angry, happy, disgust, neutral, and sad. These graphs show that AUC of disgust emotion is less as compared to the rest of emotions. Figure 6 shows that the AUCs of the dataset without disgust emotion are much improved as compared to a dataset with disgust emotion. It is concluded that disgust emotion is difficult to recognize than the rest of the emotions.

The confusion matrix of the complete dataset with and without disgust emotion is shown in Figs. 7 and ​ and8 8 respectively, where actual and predicted emotions are listed on vertical and horizontal axis, respectively. As can be seen from Fig. 7 , the disgust emotion is the most wrongly predicted class which results in reduction of system accuracy. The confusion matrix without the disgust emotion in Fig. 8 shows a reduction in misclassification of the emotion which thereby results in enhanced accuracy of the system.

This study presented the design and development of emotional speech corpus for the Urdu language. For the development of this corpus , five sentences in the Urdu language were simulated in five different emotions, that is, happy, sad, angry, disgust, and neutral. The recognition of emotions from Urdu speech signals using different machine learning techniques was carried out. The Urdu emotional speech data of opposite genders obtains different recognition rates. Different feature sets were studied for better classification of emotions, and only those features were adopted that show a good description of the speech signals. The experimental results showed that males have distinct emotions as compared to the female emotions. There was a large difference in the model performance with disgust and without disgust emotion. The maximum overall recognition accuracy achieved with disgust emotion was 72.5% with k-NN, 68.5% with one-against-rest classifier, and 66.2% on k-NN for male, female, and the complete dataset, respectively. For the dataset without disgust emotion, maximum overall recognition accuracy was 82.5% with k-NN, 78.5% with one-against-rest classifier, and the 76.5% on k-NN for male, female, and the complete dataset respectively.

This study could potentially play a vital role in the automatic human behavior analysis for Urdu speakers. Some of the use cases of the proposed study in human behavior analysis are assessing candidates’ suitability for a job, assessing emotional intelligence, lie detection, etc. In future, we are devoted to developing a more robust Urdu dataset with more emotions and human behaviors.

Supplemental Information

Supplemental information 1.

The complete dataset with separate files for the male dataset and the female dataset.

Funding Statement

The authors received no funding for this work.

Additional Information and Declarations

The authors declare that they have no competing interests.

Awais Asghar conceived and designed the experiments, performed the experiments, analyzed the data, performed the computation work, prepared figures and/or tables, authored or reviewed drafts of the paper, and approved the final draft.

Sarmad Sohaib conceived and designed the experiments, performed the experiments, performed the computation work, prepared figures and/or tables, and approved the final draft.

Saman Iftikhar analyzed the data, prepared figures and/or tables, and approved the final draft.

Muhammad Shafi analyzed the data, authored or reviewed drafts of the paper, and approved the final draft.

Kiran Fatima performed the computation work, authored or reviewed drafts of the paper, proof reading and final touch to the paper, and approved the final draft.

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