kazakh text to speech

Kazakh Text to Speech

Easily convert text to speech in Kazakh, and 90 more languages. Try our Kazakh text to speech free online. No registration required. Create Audio

Text to speech Kazakh voices are natural, realistic and life-like, and you can use them to quickly create language lessons, voiceovers and audio clips for audiences in Kazakhstan. Kazakhstan text reader voices are more convenient, faster and cheaper than hiring Kazakh voice talent. Make videos from Powerpoint presentations, or turn Word documents into MP3 files in minutes.

Kazakh is a Turkic language spoken by approximately 18 million people, mostly in Kazakhstan but also in north-western China and western Mongolia. It’s currently written using Cyrillic script, with a planned transition to Latin script over the next few years. Currently, Narakeet Kazakh voices can only read Cyrillic script correctly. We plan to add support for Latin script in the future.

Text to speech Kazakh voices

Narakeet has 4 Kazakh text to speech male and female voices. Play the video below (with sound) for a quick demo.

Making content for Kazakhstan? In addition to our Kazakh speech synthesis, see also our Uzbek text to speech voices

Kazakh Voice Over

In addition to these voices, Narakeet has 700 text-to-speech voices in 90 languages .

For more options (uploading Word documents, voice speed/volume controls, working with Powerpoint files or Markdown scripts), check out our Tools .

Kazakh voice synthesis

Kazakh text to speech synthesizers make it easy to produce lots of different types of audio and video materials for Kazakh audiences, including:

• Kazakhstan language audio
• TTS Kazakh voice messages
• Kazakh TTS Social media stories
• Kazakh language YouTube Text to speech videos

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

Free Kazakh Text to Speech

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• Convert any text into audio
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• Text to Speech
• Kazakh Text to Speech

Text to Speech Kazakh

Use our online Kazakh text to speech if you are in Kazakhstan or wherever in the world you are and speak Kazakh. Speechify has the most natural, native-sounding Kazakh voices. Try pasting your content, or typing it in and then choose male or female Kazakh voice and begin listening.

Optionally, you can download your Kazakh text to speech as an MP3 or other format.

Kazakh Text to Speech Features

Ditch robotic voices for Speechify’s native-sounding Kazakh text to speech.

The Best Kazakh Text to Voice Converter

sten up to 9x faster with Speechify’s ultra realistic Kazakh text to speech software that lets you read faster than the average reading speed, without skipping out on the best AI voices.

Listen & Read at the Same Time

With Speechify text highlighting you can choose to just listen, or listen and read at the same time. Easily follow along as words are highlighted – like Karaoke. Listening and reading at the same time increases comprehension.

Convert Kazakh Text to Studio-Quality Voices

With Speechify’s easy-to-use AI Kazakh text to speech voices, you can forget about warbly robotic text to speech AI voices. Our accurate human-like AI Kazakh voices are all HD quality and native sounding.

Kazakh Image to Speech

Scan or take a picture of any image and Speechify will read it aloud to you with its cutting-edge Kazakh OCR technology. Save your images to your library in the cloud and access it anywhere. You can now listen to that note you got from a friend, relative, or other loved one.

Text to Speech in these Kazakh Voices

The most realistic Kazakh TTS voices only on the best text to speech app.

Gwyneth Paltrow

Try Kazakh Text to Speech in these Popular Voices

Kazakh Text to Speech Apps & Extensions

Turn any Kazakh  text into natural sounding audio instantly in your browser, smartphone, or Mac

What is Kazakh Text to Speech Section

Kazakh Text to speech, also known as TTS, read aloud, or even speech synthesis. It simply means using artificial intelligence to read words aloud be; it from a PDF, email, docs, or any website. There isn’t a voice artist recording phrases or words, or even the entire article. Speech generation is done on-the-fly, in real time, with natural sounding AI voices.

And that’s the beauty of it all. You don’t have to wait. You simply press play and artificial intelligence makes the words come alive instantly, in a very natural sounding voice. You can change voices and accents across multiple languages.

I used to hate school because I’d spend hours just trying to read the assignments. Listening has been totally life changing. This app saved my education.

Speechify has made my editing so much faster and easier when I’m writing. I can hear an error and fix it right away. Now I can’t write without it.

Speechify makes reading so much easier. English is my second language and listening while I follow along in a book has seriously improved my skills.

Get Kazakh Text to Speech Today

And begin removing barriers to reading Kazakh online

More Text to Speech Features You’ll Love

Speechify text to speech online reviews, kate marfori.

Product Manager at The Star Tribune

With Speechify’s API, we can offer our users a new and accessible way to consume our content. We’ve seen that readers who choose to listen to articles with Speechify are on average 20% more engaged than users who choose not to listen.

Susy Botello

Thanks for sharing this.I love this feature. I just tweeted at you on how much I like it. The voice is great and not at all like the text-to-speech I am used to listening to. I am a podcaster and I think this will help a lot of people multitask a bit, especially if they are interrupted with incoming emails or whatever. You can read-along but continue reading if your eyes need to go elsewhere. Hope you keep this. It’s already in other web publications. I also see it in some news sites. So I think it could become a standard that readers expect when they read online. Can I vote twice?

Renato Vargas

I just started using Medium more and I absolutely love this feature. I’ve listened to my own stories and the Al does the inflections just as I would. Many complain that they can’t read their own stories, but let’s be honest. How many stories would go without an audio version if you had to do all of them yourself? I certainly appreciate it. Thanks for this!!

Oh! How cool – I love it 🙂 The voice is surprisingly natural sounding! My eyes took a much appreciated rest for a bit. I’ve been a long time subscriber to Audible on Amazon. I think this is Great 🙂 Thank you!

Paola Rios Schaaf

Super excited about this! We are all spending too much time staring at our screens. Using another sense to take in the great content at Medium is awesome.

Hi Warren, I am one of those small, randomly selected people, and I ABSOLUTELY love this feature. I have consumed more ideas than I ever have on Medium. And also as a non-native English speaker, this is really helping me to improve my pronunciation. Keep this forevermore! Love, Ananya:)

This is the single most important feature you can role out for me. I simply don’t have the time to read all the articles I would like to on Medium. If I could listen to the articles I could consume at least 3X the amount of Medium content I do now.

Andrew Picken

Love this feature Warren. I use it when I’m reading, helps me churn through reading and also stay focused on the article (at a good speed) when my willpower is low! Keeping me more engaged..

I was THRILLED the other day when I saw the audio option. I didn’t know how it got there, but I pressed play, and then I was blown away hearing the words that I wrote being narrated

Neeramitra Reddy

LOVE THISSS. As someone who loves audio almost as much as reading, this is absolute gold

What is Kazakh text to speech (TTS)?

Text-to-speech goes by a few names. Some refer to it as TTS,  read aloud , or even speech synthesis ; for the more engineered name. Today, it simply means using  artificial intelligence  to read words aloud be; it from a PDF, email, docs, or any website. Instantly turn text into audio. Listen in English, Italian, Portuguese,  Spanish , or more and choose your accent and character to personalize your experience.

How does Kazakh AI text to speech work?

Beautifully. Kazakh Speech synthesis works by installing an app like Speechify either on your device or as a browser extension. AI scans the Kazakh words on the page and reads it out loud, without any lag. You can change the default voice to a custom voice, change accents, languages, and even increase or decrease the speaking rate.

AI has made significant progress in synthesizing voices. It can pick up on formatted text and change tone accordingly. Gone are the days where the voices sounded robotic. Speechify is revolutionizing that.

Once you install the TTS mobile app, you can easily convert Kazakh text to speech from any website within your browser, read aloud your email, and more. If you install it as a browser extension, you can do just the same on your laptop. The web version is OS agnostic. Mac or Windows, no problem.

What is the Kazakh text-to-speech service?

A Kazakh text-to-speech service is a tool, like Speechify text to speech, that transforms your written Kazakh words into spoken words. Imagine typing out a message in Kazakh and having it read out loud by a digital voice – that’s what TTS services, like Speechify TTS do.

What are the benefits of Kazakh text to speech?

Kazakh TTS technology offers many benefits, like helping those with reading difficulties, providing rest for your eyes, multitasking by listening to content, improving pronunciation and language learning, and making content accessible to a wider audience.

How is Speechify TTS better than Murf AI text to speech, Google Voice, or TTSReader?

Speechify Kazakh TTS stands out by offering a more natural and human-like voice quality, a wider range of customization options, and user-friendly integration across devices. Plus, our dedication to accessibility means that we ensure a seamless and inclusive experience for all Kazakh users.

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Kazakh Text to Speech

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

Elevate your content with LOVO's TTS voices, easily generating high-quality voiceovers for videos, marketing, and presentations, and more.

How Kazakh 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 available. 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

Increase visibility

Go global with your content.

With LOVO's Kazakh text to speech generator, you can convert your scripts into lifelike voiceovers in more than 100 languages. Expand your audience reach by transforming your content with TTS in just seconds, all from your web browser. In a few simple steps, you can create content in multiple languages and connect with a global audience. Just input your script, choose a voice, click generate, and download it as an MP3 or WAV. Take it a step further by using our online video editor, powered by advanced AI tools, to add captivating Kazakh subtitles and more to your videos.

Versatile TTS

Realistic kazakh accent voices for all your needs..

Discover a variety of male and female voices with authentic Kazakh accents to match your content needs. Each voice profile offers a preview, allowing you to make the perfect selection. Our Kazakh TTS is ideal for a range of applications, including training materials, product demos, marketing videos, sales presentations, games, and animations. Unleash your creativity and explore endless possibilities as our text-to-speech voices are also available in multiple languages, ready to be generated in just minutes.

Natural voices

Human-like realistic text to speech with a kazakh accent..

With LOVO's TTS generator, easily create natural-sounding voiceovers using AI voices with Kazakh accents. The Kazakh TTS converter is user-friendly and efficient. Input or type your script, select your desired voice, generate the audio, and download it in MP3 or WAV format. Moreover, LOVO offers a wide range of over 500 human voices in 100 languages, enabling you to enhance your content with just a few simple clicks.

How do you convert Kazakh 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 kazakh tts generated in genny, discover more.

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Text to Speech

Kazakh Text to Speech Conversion

Language code: kk-KZ

Embrace cutting-edge synthesis for authentic Kazakh speech output.

Kazakh (kz-KZ or kk-KZ), a Turkic language spoken primarily in Kazakhstan, possesses several unique pronunciation features that distinguish it from other languages. Here are some of its noteworthy phonetic characteristics:

Consonant-rich. Kazakh has a set of consonants that might be unfamiliar to speakers of European languages. For instance, the language distinguishes between voiced and voiceless sounds, and it features specific sounds like the voiced uvular fricative [ʁ] and the voiceless uvular stop [q].

Vowel Harmony. Like other Turkic languages, Kazakh follows a pattern of vowel harmony. This means that vowels within a word harmonize to be either front or back. This phonological feature influences the pronunciation of suffixes and can determine the overall sound of words.

Stress Patterns. In Kazakh, stress is usually fixed on the last syllable of a word, which is a defining rhythmic characteristic of the language.

Affricates. The language has specific affricate sounds that combine a plosive and a fricative, such as "ч" [tʃ] as in "chay" (tea).

This is where SpeechGen comes into play. We harness the power of artificial intelligence and neural networks to ensure that the conversion of Kazakh text into speech is as natural as possible. Our system pays close attention to the nuances of grammar, specific features of pronunciation, and the overall rhythm of the language.

• Countries: Kazakhstan, Russia, China, Uzbekistan, Turkey, Mongolia and other countries.
• Kazakh is spoken by 12 million people
• There are more than 166 thousand words in the language dictionary

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Kazakh Text To Speech

Use Speakatoo to effortlessly convert Kazakh text to speech and get access to natural-sounding male/female AI voices.

Free Signup to download

How to Convert Kazakh Text to Speech?

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

1. Choose a language

Select the Kazakh language from the list or experience Speakatoo's text to speech conversion in 120+ languages.

2. Select any Male/Female Voice

Quickly preview any voice and select one as per your requirement. You may toggle between available voice tones and check pronunciations before converting your 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 Advance Effects

Set Rate, Pitch or Volume from Audio Control. Additionally, you may also apply voice effects like Angry, Cheerful, Excited, Shouting Whispering, Friendly, Hopeful, Sad etc.

5. Choose desired output file format

You may generate output files in any file format like mp3, wav, mp4, ogg & flac.

6. Click on Synthesize & Download

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

Why Choose Us

• Easy to use : Speakatoo's text to speech tool provides a simple interface for inputting text and converting it into speech.
• Multiple language support : In addition to Kazakh, Speakatoo supports multiple languages, allowing users to switch between different languages effortlessly.
• Accurate speech conversion : Our platform is designed to accurately convert written text into speech, ensuring that the audio output is of the highest quality.
• Affordable pricing : Speakatoo offers competitive pricing for its services, with different pricing plans to suit the needs and budgets of different users.

Features of Kazakh Text to Speech Converter

Kazakh Voice-over Videos

Generate Kazakh voice-overs with our converter and engage your local audience.

Kazakh Social Media Stories

Create scripts, and convert them into audio for your social media with a clear Kazakh accent.

Kazakh Content Creation

Kazakh text to speech can help you create audio content for your audience.

Kazakh YouTube Videos

Create content for youtube in Kazakh language and reach more audience.

Frequently Asked Questions

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

Speakatoo's Kazakh Text to Speech platform converts Kazakh text into human-like voices, letting users create voice audio files from typed text. These files find uses in e-learning, video production, presentations, and more.

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.

Is SSML (Speech Synthesis Markup Language) supported?

Speakatoo fully supports SSML, enabling users to fine-tune the speech parameters such as rate, pitch and volume. This feature offers greater control and customization options for the generated audio.

Does Speakatoo support different languages other than Kazakh?

Yes, Speakatoo's text to speech supports several languages besides Kazakh. Our platform currently supports over 120 languages and 850 voices, including Chinese, Spanish, German, Swedish, French and more.

Can I download the generated 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.

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

Certainly! Speakatoo's text to speech Kazakh 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

Get newest information from our social media platform

Tegeurin AI

Tegeurin's AI solution integrates essential technologies, including computer vision, speech recognition, natural language processing, semantic knowledge representation, and deep learning. The platform operates as a flexible, open architecture, providing support to both internal business needs and external partnerships with developers. This allows Tegeurin to drive the adoption of AI technology across various industries, promoting growth and driving progress.

AI Writing (Beta)

Create bold, clear, and mistake-free content effortlessly in Kazakh and let our advanced technology assist you every step of the way. Say goodbye to writing challenges in Kazakh and hello to effective and impactful communication with our tool.

Tegeurin AI engine uses the breakthrough end-to-end synthesis solution in the field of synthesis to provide high-fidelity, personalized, natural listening, and multi-style audios to meet the needs of different scenarios and it is widely used in novels, audio books, and short videos, automobile, education, customer service, finance, government, and others.

Text to Speech

Card subtitle.

Text-to-speech is a technology that converts written text into spoken words, enabling businesses to create high-quality voiceovers for their content

Intelligent Writing

Intelligent writing involves using AI and machine learning to assist or automate different aspects of writing, such as grammar checking and content optimization.

Morphological Analyzer

A morphological analyzer is a software tool used in computational linguistics that analyzes the morphemes or smallest units of meaning in a word and breaks it down into its constituent parts, such as roots, prefixes, and suffixes.

Morphological Disambiguation

Morphological disambiguation is the process of determining the correct meaning of a word that has multiple possible interpretations based on its morphology or structure.

Named Entity Recognition

Named Entity Recognition (NER) is a technology used in various industries, such as finance, healthcare, and e-commerce, to automatically extract and classify important information from unstructured text, which can improve efficiency and accuracy in tasks such as document processing, customer support

Speech to Text

Speech to Text is a technology that converts spoken language into text, which can be useful in various industries, such as telecommunications, healthcare, and media, to improve accessibility, automate transcription, and analyze speech data.

A corpus is a structured and comprehensive collection of written or spoken language that is carefully selected and compiled for linguistic purposes. It serves as a representative sample of a language or language variety, and may include millions of words or more. Corpora are typically used in comput

Topic Modeling

Topic modeling is a powerful tool for businesses that want to extract valuable insights and meaning from large and complex textual data.

Speaker Diarization

Speaker diarization is a process of separating an audio or video recording into distinct segments based on the identity of the speaker. It is commonly used in speech processing and natural language understanding applications to enable accurate speech recognition, speaker identification, and language

Speaker Identification

Speaker identification is the process of identifying the individual speaker from an audio or speech signal based on their unique voice characteristics. It is widely used in various industries for applications such as speech recognition, forensic analysis, and security authentication.

OCR (Optical Character Recognition) is a technology that enables computers to read and convert printed or handwritten text into digital format. It is commonly used in various industries for applications such as document scanning, data entry, and text recognition.

Recommendation

Recommendation is a technology that analyzes user data and preferences to provide personalized suggestions for products or services. It is widely used in e-commerce, entertainment, and social media industries to enhance the user experience and increase customer satisfaction.

Automatic Summarization

Automatic summarization condenses text using algorithms and NLP. It improves retrieval and productivity, with extractive and abstractive methods. Challenges include capturing nuances and ensuring coherence.

POS Tagging

POS tagging assigns grammatical tags to words in a text. It helps understand sentence structure and aids in NLP tasks like sentiment analysis.

Question Answering

Question Answering (QA) is an NLP task where computers answer questions in human language. It involves understanding the question, extracting relevant information, and generating accurate answers.

Token and Sentence Segmentation

Token and sentence segmentation are key tasks in natural language processing. Token segmentation splits text into words or tokens, aiding analysis. Sentence segmentation divides text into sentences, facilitating parsing and processing. They are vital for NLP applications.

Parsing is a technique in natural language processing that analyzes sentence structure. It creates a hierarchical representation, like a parse tree or dependency graph, showing word relationships. Parsing is essential for tasks like information extraction, translation, and grammar checking.

Machine Translation

Machine Translation (MT) automates text or speech translation between languages using AI. By analyzing source language input, MT systems generate corresponding output in the target language. It's vital for cross-lingual communication across domains like business, education, and global collaboration.

LLM for Kazakh

A large language model (LLM) like ChatGPT is a powerful AI system trained on vast amounts of text data. It can understand and generate human-like text, assisting with a wide range of language-related tasks.

Research Team

Toleu alymzhan, gulmira tolegen, rustam mussabayev, alexander krassovitskiy, iskander akhmetov.

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An expanded version of the previously released Kazakh text-to-speech (KazakhTTS) synthesis corpus. In KazakhTTS2, the overall size has increased from 93 hours to 271 hours, the number of speakers has risen from two to five (three females and two males), and the topic coverage has been diversified.

IS2AI/Kazakh_TTS

Folders and files, repository files navigation, kazakhtts recipe.

This is the recipe of Kazakh text-to-speech model based on KazakhTTS and KazakhTTS2 corpora.

Setup and Requirements

Our code builds upon ESPnet , and requires prior installation of the framework. Please follow the installation guide and put the KazakhTTS folder inside espnet/egs2/ directory:

Go to Kazakh_TTS/tts1 folder and create links to the dependencies:

Downloading the dataset

Download KazakhTTS dataset and untar in the directory of your choice. Specify the path to the dataset directory (where Audio/Transcripts dirs are located) inside KazakhTTS/tts1/local/data.sh script:

For example db_root=/home/datasets/ISSAI_KazakhTTS/M1/Books

To train the models, run the script ./run.sh inside KazakhTTS/tts1/ folder. GPU and RAM specifications can be found in the configuration ( conf/ ) folder.

If you would like to train fastspeech/transformer models, change train_config=conf/train.yaml accordingly. The detailed description of each stage are documented in ESPNet's repository.

Pretrained models

The model was developed by the Institute of Smart Systems and Artificial Intelligence, Nazarbayev University Kazakhstan (henceforth ISSAI).

Please use the model only for a good cause and in a wise manner. You must not use the model to generate data that are obscene, offensive, or contain any discrimination with regard to religion, sex, race, language or territory of origin.

ISSAI appreciates and requires attribution. An attribution should include the title of the original paper, the author, and the name of the organization under which the development of the model took place. For example:

Mussakhojayeva, S., Janaliyeva, A., Mirzakhmetov, A., Khassanov, Y., Varol, H.A. (2021) KazakhTTS: An Open-Source Kazakh Text-to-Speech Synthesis Dataset. Proc. Interspeech 2021, 2786-2790, doi: 10.21437/Interspeech.2021-2124. The Institute of Smart Systems and Artificial Intelligence (issai.nu.edu.kz), Nazarbayev University, Kazakhstan

kaztts_female1_tacotron2_train.loss.ave

• https://issai.nu.edu.kz/wp-content/uploads/2022/03/kaztts_female1_tacotron2_train.loss.ave.zip

kaztts_female2_tacotron2_train.loss.ave

• https://issai.nu.edu.kz/wp-content/uploads/2022/03/kaztts_female2_tacotron2_train.loss.ave.zip

kaztts_female3_tacotron2_train.loss.ave

• https://issai.nu.edu.kz/wp-content/uploads/2022/03/kaztts_female3_tacotron2_train.loss.ave.zip

kaztts_male1_tacotron2_train.loss.ave

• https://issai.nu.edu.kz/wp-content/uploads/2022/03/kaztts_male1_tacotron2_train.loss.ave.zip

kaztts_male2_tacotron2_train.loss.ave

• https://issai.nu.edu.kz/wp-content/uploads/2022/03/kaztts_male2_tacotron2_train.loss.ave.zip

Pretrained vocoders

Parallelwavegan_female1_checkpoint.

• https://issai.nu.edu.kz/wp-content/uploads/2022/03/parallelwavegan_female1_checkpoint.zip

parallelwavegan_female2_checkpoint

• https://issai.nu.edu.kz/wp-content/uploads/2022/03/parallelwavegan_female2_checkpoint.zip

parallelwavegan_female3_checkpoint

• https://issai.nu.edu.kz/wp-content/uploads/2022/03/parallelwavegan_female3_checkpoint.zip

parallelwavegan_male1_checkpoint

• https://issai.nu.edu.kz/wp-content/uploads/2022/03/parallelwavegan_male1_checkpoint.zip

parallelwavegan_male2_checkpoint

• https://issai.nu.edu.kz/wp-content/uploads/2022/03/parallelwavegan_male2_checkpoint.zip

Speech synthesis

You can synthesize an arbitrary text using synthesize.py script. Modify the following lines in the script:

Now you can run the script using an arbitrary text, for example:

The generated file will be saved in tts1/synthesized_wavs folder.

Contributors 4

• Shell 67.5%
• Python 32.5%

• Kazakh (Kazakhstan) Text to Speech

Text-to-speech Kazakh (Kazakhstan) by TTSFree. Online speech synthesis with natural sounds, and lifelike voices. Free mp3 download.

Select language and regions to speech.

• TTS Server 1
• TTS Server 2

Voice Pitch

Audjust voice speed, 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

Aigul, Female

Daulet, Male

Text to speech kazakh (kazakhstan) usecases.

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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.

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TTS is the abbreviation for Text to Speech, a technology text-to-speech. It has different applications, both free and paid. It can be used to create voiceover for videos, convert text documents into voices or help people with vision problems have can "read" the text.

What is the best free text to speech (software, apps ) ?

Free text to speech apps to convert any text to audio. The best free text to speech software has a lot of use cases in your computing life. The best free text-to-speech program or software can convert your text into voice/speech with just a few seconds. We suggest some listings of the best free text-to-speech that provides natural sound for your project.

• #1 TTSFree.com
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We use the best AI from Google Cloud, Microsoft, Amazon Polly, Watson IBM Cloud and several other sources

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Yes, Free Text to Speech! Provide the highest quality free TTS service on the Internet. Covert text to speech, MP3 file. You can listen or download it. Supports English, French, German, Japanese, Spanish, Vietnamese... multiple languages. Besides the free plan, we have paid plans with advanced features, increased limits, and best voice quality.

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Most of the text-to-speech tools work similarly. You must type the text you want to convert to voice or copy from the text file into the input box. Then you have to select the voices available and preview the audio. We are talking Kazakh (Kazakhstan) here, so you need to choose the language and accent of the Kazakh (Kazakhstan). Once you find the most suitable voice, you can generate and download the mp3 file.

Kazakh (Kazakhstan) Speech Synthesis Markup Language (SSML) support?

Full SSML support. You can send Speech Synthesis Markup Language (SSML) in your Kazakh (Kazakhstan) Text-to-Speech request to allow for more customization in your audio response by providing details on pauses, and audio formatting for acronyms, dates, times, abbreviations, addresses, or text that should be censored. See the Speech-to-Text SSML tutorial for more information and code samples.

Convert text to speech online free unlimited?

With the basic or premium plan, we offer unlimited Kazakh (Kazakhstan) Text-to-speech. It includes unlimited number of converted characters, number of conversions. You can create a lot of text-to-speech conversions without any limitations.

The cost of text to speech systems has dropped dramatically in recent years— much faster than most anticipated. As a result, these systems are now accessible to the general public without requiring any financial means or technical expertise. Anyone with an internet connection and an audio device can create their own text to speech system. No technical knowledge is required whatsoever; only an internet connection and web browser.

Kazakhstani Kazakh

Use our Kazakhstani Kazakh text to speech AI voice generator. Convert text to voice in Kazakhstani Kazakh using AI and download as MP3 or WAV audio files.

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2 Text to Speech Kazakhstani Kazakh Accents (TTS Kazakhstani Kazakh)

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Kazakh Speech Corpus 2

Kazakh Speech Corpus 2 (KSC2) is the first industrial-scale open-source Kazakh speech corpus. KSC2 corpus subsumes the previously introduced two corpora: Kazakh speech corpus and Kazakh Text-To-Speech 2, and supplements additional data from other sources like tv programs, radio, senate, and podcasts. In total, KSC2 contains around 1.2k hours of high-quality transcribed data comprising over 600k utterances.

Importantly, KSC2 contains utterances with the Kazakh-Russian code-switching, a common conversation practice among Kazakh speakers.

The dataset can be used by professionals to develop various Kazakh speech and language processing applications, such as virtual assistants in the Kazakh language, robots speaking Kazakh, smart homes and cars, voice and text-enabled applications that can also assist people with special needs, and many more.

Like the first version, the KSC2 dataset is freely available to both academic researchers and industry practitioners from ISSAI website.

If you use the ISSAI Kazakh Speech Corpus 2 for commercial purposes, please add this statement to your product or service:

Our product uses ISSAI Kazakh Speech Corpus 2 ( https://doi.org/10.48342/m90y-aj02 ), which is available under a Creative Commons Attribution 4.0 International License.

If you use the ISSAI Kazakh Speech Corpus 2 for research, please cite it as:

Mussakhojayeva, S., Khassanov, Y. , Varol, H.A.: KSC2: An Industrial-Scale Open-Source Kazakh Speech Corpus. In: Proceedings of the 23rd INTERSPEECH Conference: pp. 1367-1371. 2022.

Here is the demo of the automatic speech recognition system build using Kazakh Speech Corpus. Please click the “RECORD” button and speak immediately until the countdown reaches zero. The recognized output will be displayed above the “RECORD” button after 10 seconds. Please note that some browsers don’t support the audio recording features.

• Click the “RECORD” button and speak immediately (in Kazakh language) until the countdown reaches zero
• The recognized output will be displayed above the “RECORD” button after 10 seconds

In some models of browsers technology of audio records is not supported. If this is your case, please, consider using up-to-date browsers in desktop devices.

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Kazakh (Kazakhstan) Text to Speech Converter

Text-to-speech kazakh (kazakhstan) 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
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Background music

How to convert text into speech with kazakh (kazakhstan) accent.

• Type some text or paste your content
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• Click the blue " Convert Now " button to start converting
• Play and Download MP3

Text to speech Kazakh (Kazakhstan) 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 Kazakh (Kazakhstan) videos and audio files for different purposes without needing a license. You can also see what people usually do with Kazakh (Kazakhstan) accents through some of these suggestions:

• Free Kazakh (Kazakhstan) Text to Speech 2024
• Text to speech Kazakh voices realistic
• Kazakhstani Kazakh text to speech AI voice generator
• Kazakh language Text-to-Speech technology
• Open-Source Kazakh Text-to-Speech Synthesis
• Kazakh (Kazakhstan) Text to Speech
• Text to speech online Kazakh (Kazakhstan) videos
• Kazakh (Kazakhstan) text to speech audiobooks
• Kazakh (Kazakhstan) voice over
• Kazakh (Kazakhstan) voice AI
• TTS Kazakh (Kazakhstan) YouTube videos
• Kazakh (Kazakhstan) text to speech TikTok videos
• Kazakh (Kazakhstan) TTS social media stories
• Kazakh (Kazakhstan) text to speech software audio messages

Frequently Asked Questions about Kazakh (Kazakhstan) 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.

K azakh TTS 2: Extending the Open-Source K azakh TTS Corpus With More Data, Speakers, and Topics

Saida Mussakhojayeva , Yerbolat Khassanov , Huseyin Atakan Varol

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[KazakhTTS2: Extending the Open-Source Kazakh TTS Corpus With More Data, Speakers, and Topics](https://aclanthology.org/2022.lrec-1.578) (Mussakhojayeva et al., LREC 2022)

• KazakhTTS2: Extending the Open-Source Kazakh TTS Corpus With More Data, Speakers, and Topics (Mussakhojayeva et al., LREC 2022)
• Saida Mussakhojayeva, Yerbolat Khassanov, and Huseyin Atakan Varol. 2022. KazakhTTS2: Extending the Open-Source Kazakh TTS Corpus With More Data, Speakers, and Topics . In Proceedings of the Thirteenth Language Resources and Evaluation Conference , pages 5404–5411, Marseille, France. European Language Resources Association.

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Common Questions Asked Related to Kazakh (Kazakhstan Accent) Text to Speech

• Q. What is Kazakh (Kazakhstan Accent) text-to-speech (TTS)?

Kazakh (Kazakhstan Accent) Text-to-Speech (TTS) is a technology that converts written Kazakh (Kazakhstan Accent) text into spoken words in a specified language, using natural-sounding voices generated by computers.

• Q. What is the best Kazakh (Kazakhstan Accent) text to speech software with natural voices?

The best Kazakh (Kazakhstan Accent) text to speech software with natural voices is TextoSpeech. It offers high-quality voice output, making your text sound natural and engaging to your audience.

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• SPEECH SYNTHESIS
• TEXT-TO-SPEECH SYNTHESIS

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Add a method, remove a method, edit datasets, kazemotts: a dataset for kazakh emotional text-to-speech synthesis.

1 Apr 2024  ·  Adal Abilbekov , Saida Mussakhojayeva , Rustem Yeshpanov , Huseyin Atakan Varol · Edit social preview

This study focuses on the creation of the KazEmoTTS dataset, designed for emotional Kazakh text-to-speech (TTS) applications. KazEmoTTS is a collection of 54,760 audio-text pairs, with a total duration of 74.85 hours, featuring 34.23 hours delivered by a female narrator and 40.62 hours by two male narrators. The list of the emotions considered include "neutral", "angry", "happy", "sad", "scared", and "surprised". We also developed a TTS model trained on the KazEmoTTS dataset. Objective and subjective evaluations were employed to assess the quality of synthesized speech, yielding an MCD score within the range of 6.02 to 7.67, alongside a MOS that spanned from 3.51 to 3.57. To facilitate reproducibility and inspire further research, we have made our code, pre-trained model, and dataset accessible in our GitHub repository.

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Tasks edit add remove, datasets edit, results from the paper edit, methods edit add remove.

GAN acoustic model for Kazakh speech synthesis

• Published: 15 April 2021
• Volume 24 , pages 729–735, ( 2021 )

Cite this article

• Arman Kaliyev 1 ,
• Bassel Zeno 2 ,
• Sergey V. Rybin 2 ,
• Yuri N. Matveev   ORCID: orcid.org/0000-0001-7010-1585 2 &
• Elena E. Lyakso 1  

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Recent studies on the application of generative adversarial networks (GAN) for speech synthesis have shown improvements in the naturalness of synthesized speech, compared to the conventional approaches. In this article, we present a new framework of GAN to train an acoustic model for speech synthesis. The proposed GAN consists of a generator and a pair of agent discriminators, where the generator produces acoustic parameters taking into account linguistic parameters; and the pair of agent discriminators are introduced to improve the naturalness of the synthesized speech. We feed the agents with acoustic and linguistic parameters, thereby the agents do not only examine the acoustic distribution, but also the relationship between linguistic and acoustic parameters. Training and testing were conducted on the Kazakh speech corpus. According to the results of this research, the proposed framework of GAN improves the accuracy of the acoustic model for the Kazakh text-to-speech system.

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Acknowledgements

The study is financially supported by the Russian Science Foundation (Project No 18-18-00063) and the Russian Foundation for Basic Research (Project 19-57-45008–IND_ a).

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Arman Kaliyev & Elena E. Lyakso

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Correspondence to Yuri N. Matveev .

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Kaliyev, A., Zeno, B., Rybin, S.V. et al. GAN acoustic model for Kazakh speech synthesis. Int J Speech Technol 24 , 729–735 (2021). https://doi.org/10.1007/s10772-021-09840-0

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Received : 24 April 2020

Accepted : 31 March 2021

Published : 15 April 2021

Issue Date : September 2021

DOI : https://doi.org/10.1007/s10772-021-09840-0

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KazEmoTTS: A Dataset for Kazakh Emotional Text-to-Speech Synthesis

This study focuses on the creation of the KazEmoTTS dataset, designed for emotional Kazakh text-to-speech (TTS) applications. KazEmoTTS is a collection of 54,760 audio-text pairs, with a total duration of 74.85 hours, featuring 34.23 hours delivered by a female narrator and 40.62 hours by two male narrators. The list of the emotions considered include “neutral”, “angry”, “happy”, “sad”, “scared”, and “surprised”. We also developed a TTS model trained on the KazEmoTTS dataset. Objective and subjective evaluations were employed to assess the quality of synthesized speech, yielding an MCD score within the range of 6.02 to 7.67, alongside a MOS that spanned from 3.51 to 3.57. To facilitate reproducibility and inspire further research, we have made our code, pre-trained model, and dataset accessible in our GitHub repository. Keywords:  dataset, emotional TTS, emotion, Kazakh, TTS

languageresource  \DeclareAcronym asr short = ASR, long = automatic speech recognition, \DeclareAcronym cer short = CER, long = character error rate, \DeclareAcronym dtw short = DTW, long = dynamic time warping, \DeclareAcronym gpu short = GPU, long = graphics processing unit, \DeclareAcronym id short = ID, long = identifier, \DeclareAcronym mcd short = MCD, long = mel-cepstral distortion, \DeclareAcronym mos short = MOS, long = mean opinion score, \DeclareAcronym mfcc short = MFCC, long = mel-frequency cepstral coefficient, \DeclareAcronym rar short = RAR, long = relative attributes ranking, \DeclareAcronym sota short = SOTA, long = state-of-the-art, \DeclareAcronym ser short = SER, long = speech emotion recognition, \DeclareAcronym tts short = TTS, long = text-to-speech,

A Dataset for Kazakh Emotional Text-to-Speech Synthesis

Abstract content

1.   Introduction

The demanding challenges of generating high-quality synthesized speech for one or more speakers have been met by rapidly developed \ac tts systems  (Shen et al., 2017 ; Ren et al., 2020 ; Arik et al., 2017 ) . Yet, synthesized speech still faces significant difficulties in expressing paralinguistic features such as emotions.

In the area of emotional \ac tts, where the voice synthesized by a \ac tts system is to convey emotions (e.g., anger, happiness, sadness, etc.), the availability of high-quality labeled datasets remains quite limited.

As far as our knowledge extends, most publicly available emotional speech datasets primarily cover high-resource languages, such as Chinese, English, or French  (Adigwe et al., 2018 ; Costantini et al., 2014 ; Busso et al., 2008 ) . These datasets typically focus on either distinct emotional states (e.g., anger, happiness, etc.;  Costantini et al. 2014 ) or emotion polarity, ranging from absolutely negative to absolutely positive  (Cui et al., 2021 ) . They often include several narrators’ speech samples and exhibit variations in terms of total audio duration  Zhou et al. ( 2021 ); Cui et al. ( 2021 ) .

In our study, we have undertaken the pioneering task of creating an emotional \ac tts dataset for Kazakh—a low-resource language. However, its utility extends beyond this specific domain and can be applied effectively in diverse areas, including \ac ser and emotional voice conversion tasks. The dataset comprises a total of 74.85 hours of recorded high-quality speech data featuring six distinct emotional categories. The Kazakh emotional TTS (KazEmoTTS) dataset is comprised of contributions from three professional narrators, with 34.23 hours of the data provided by a female narrator and 40.62 hours by two male narrators. Additionally, we introduce a \ac tts model, trained on KazEmoTTS, with the capability to produce Kazakh speech reflecting six emotional expressions. KazEmoTTS and the model are openly accessible for both academic and commercial purposes, operating under the provisions of the Creative Commons Attribution 4.0 International License in our GitHub repository. 1 1 1 https://github.com/IS2AI/KazEmoTTS

The structure of the paper is as follows: Section 2 offers an overview of previous research in emotional \ac tts. Section 3 describes the construction of the dataset. Section 4 covers the experimental design and evaluation metrics. Section 5 provides a presentation of the experimental results and a brief summary of the main findings. Section 6 concludes the paper.

2.   Related Work

Previous studies into the complex relationships and interactions between distinct emotional states suggest that individuals can potentially experience a wide array of diverse emotions  (Plutchik, 2001 ; Braniecka et al., 2014 ) .   Plutchik and Kellerman ( 2013 ) distill a set of eight fundamental emotions, including anger, anticipation, disgust, fear, joy, sadness, surprise, and trust, with other emotional states believed to arise from various combinations of these.

That said, Paul Ekman’s well-known theory of six basic emotions  (Ekman, 1992 ) proposes the existence of anger, disgust, fear, happiness, sadness, and surprise, and is frequently invoked in emotional \ac tts research  (Schröder, 2009 ; Zhou et al., 2020 , 2022a ) .

Most datasets employed for emotional \ac tts include emotion labels, in contrast to prosody modeling approaches  (Du and Yu, 2021 ; Guo et al., 2022b ) , which do not rely on preset labels. Presently, emotional \ac tts research primarily revolves around two methods: (1) synthesizing speech with explicit, predefined emotional labels and (2) regulating the intensity of emotions in speech synthesis.

Employing hard-labeled emotions is generally considered the most straightforward approach.  Lee et al. ( 2017 ) utilized an attention-based decoder that captures an emotion label vector to generate the desired emotional style in the synthesized speech. In  Kim et al. ( 2021 ) , style embeddings were extracted from both a reference speech sample and a corresponding style tag.

With respect to models that allow for the control of emotional intensity, the prevailing method of determining emotional intensity is \ac rar  (Parikh and Grauman, 2011 ) . RAR involves the creation of a ranking matrix that is derived through a max-margin optimization problem typically addressed using support vector machines. The solution is subsequently employed for model training. However, it is important to note that this process is manually constructed and therefore can potentially introduce biases into the training process  (Guo et al., 2022a ) .

In  Um et al. ( 2019 ) , the researchers introduced an algorithm designed to increase the gap between emotion embeddings. They also employed the interpolation of this embedding space as a means to control the intensity of emotions. In  Im et al. ( 2022 ) , quantization techniques were introduced to measure the distances between emotion embeddings, enabling the control of emotion intensities.

Similar methods have been applied to intensity control in emotion conversion  (Choi and Hahn, 2021 ; Zhou et al., 2022b ) . However, even though an autoregressive model  (Zhou et al., 2022a ) that relies on intensity values derived from \ac rar to weigh emotion embeddings is implemented, the problem of speech quality degradation persists.

EmoDiff  (Guo et al., 2022a ) , built on the design of GradTTS  Popov et al. ( 2021 ) , introduces a soft-label guidance approach inspired by the classifier guidance technique, employed in diffusion models  (Dhariwal and Nichol, 2021 ; Liu et al., 2021 ) . The classifier guidance technique is a sampling method that leverages the gradient of a classifier to lead the sampling path when provided with a one-hot class label. The adoption of an alternative approach can be observed in EmoMix  (Tang et al., 2023 ) , another GradTTS-based model. This approach combines a diffusion probabilistic model with a pre-trained \ac ser model. The emotion embeddings extracted by the \ac ser model act as an additional condition, enabling the reverse process within the diffusion model to generate primary emotions.

3.   Dataset Construction

3.1.   text collection.

Narration materials were drawn from multiple sources. Scientific, computer technology, historical, and international articles were retrieved from Kazakh Wikipedia. News content was collected from reputable Kazakh media outlets. In addition, selections from public domain books, fairy tales, and phrasebooks were included. All the collected texts were split sentence-wise.

3.2.   Recording Process

We hired three professional narrators—one female and two males—for the project. The narrators were given the option to record in their personally arranged home studios or within the facilities of our institution. They were also given precise instructions to read the texts in quiet indoor settings while conveying a high degree of emotional expression in line with the specified emotions.

Each sentence was paired with one of the six emotions selected for the study, ensuring an even distribution among all sentences. Our selection of these emotions was informed by their prevalence in prior research  (Zhou et al., 2021 ; Adigwe et al., 2018 ; Busso et al., 2008 ) and their discernibility by evaluators  (Costantini et al., 2014 ) . As a result, the list of emotions examined in our study included all those proposed in  Ekman ( 1992 ) , with the sole exception being “disgust”. Additionally, we introduced a “neutral” option, representing the absence of a specific emotion.

All recorded audio files were either sampled at a rate of 44.1 kHz and stored as 16-bit samples or at a rate of 48 kHz and stored as 24-bit samples. The whole data collection process was facilitated by a messenger application (Telegram) bot, as depicted in Figure  0(a) .

3.3.   Audio-to-Text Alignment Verification

We conducted a thorough examination of the recorded audio files using a customized version of the Whisper multilingual \ac asr  (Radford et al., 2022 ) system to assess the accuracy of the audio-to-text alignment. The \ac asr system generated transcriptions based on the audio files, which were subsequently compared with the original texts. Texts exhibiting a \ac cer were identified and subjected to a review process by a team of moderators. Recordings were excluded if they contained mispronunciations or significant background noise.

3.4.   Dataset Specification

The audio recordings and their corresponding transcriptions are organized into separate folders for each narrator. All audio recordings were downsampled to a rate of 22.05 kHz and saved in the WAV format with a 16-bit per sample configuration. They also underwent a preprocessing step involving the removal of silence and normalization, achieved by dividing the audio by its maximum absolute value. The transcripts were saved as TXT files encoded in UTF-8 variable-length character encoding standard. Both the audio and transcript files share identical filenames, differing only in their file extensions. Each file name comprises the narrator’s \ac id, emotion, and utterance \ac id, structured as narratorID_emotion_utteranceID .

The dataset contains 8,794 unique sentences and 86,496 unique words with an average sentence length of 10.83 words. Initially, a total of 84,714 audio files were recorded, but following quality checks, the dataset now contains 54,760 audio recordings. These recordings collectively represent an overall duration of 74.85 hours. The duration for the female narrator (F1) is 34.23 hours, with an average segment length of 5.0 seconds. The duration for the first male narrator (M1) is 26.51 hours, with an average audio segment length of 4.8 seconds. For the second male narrator (M2), the duration is 14.11 hours, with an average segment length of 4.9 seconds. More detailed statistics for the dataset are provided in Tables  1 and  2 .

4.   Experimental Setup

4.1.   kazemotts architecture.

We built our \ac tts model based on the design of GradTTS with hard label emotions  (Popov et al., 2021 ) , as was done in  Guo et al. ( 2022a ) and  Tang et al. ( 2023 ) . The model was trained with the Adam optimizer and a learning rate of 10 − 4 superscript 10 4 10^{-4} 10 start_POSTSUPERSCRIPT - 4 end_POSTSUPERSCRIPT for 3.7 million steps on one \ac gpu on an NVIDIA DGX A100 machine. To improve the performance of diffusion models  (Song et al., 2020 ) , we implemented exponential moving averages for the weights of the model during training. In addition, we removed the dependency on ground truth duration data and adjusted the sampling rate to 22,050 Hz. During the inference phase, we set the guidance level parameter γ 𝛾 \gamma italic_γ to 100. The output of the model was an array of 80-dimensional log mel-filter bank features, representing acoustic features. To transform these acoustic data into time-domain waveform samples, we utilized the HiFiGAN vocoder  (Kong et al., 2020 ) . Specifically, we trained it as a multi-speaker vocoder on the KazEmoTTS dataset without providing emotional labels for 1.72 million steps.

4.2.   Objective Evaluation

We employed \ac mcd  (Kubichek, 1993 ) as an objective assessment metric to evaluate the quality of the synthesized speech. This approach involves comparing the \ac mfcc vectors extracted from the generated speech and the ground truth speech, with a lower \ac mcd score suggesting that the generated speech is more similar to the ground truth. To mitigate issues arising from the potentially extreme scaling of \ac mcd due to variations in the two input speech lengths, we adopted the \ac dtw algorithm, as described in  Battenberg et al. ( 2019 ) .

4.3.   Subjective Evaluation

To evaluate the quality of the synthesized speech, we conducted a subjective evaluation survey via a messenger application (Telegram) bot. The user interface of the bot was developed in Kazakh, as shown in Figure  1 b. To recruit volunteer participants, we distributed the link to the survey on popular social media platforms.

The survey involved a two-fold evaluation process. Participants were first tasked with evaluating the naturalness of a given speech sample, focusing on its degree of human-likeness. The evaluation was conducted using a five-point scale: 1.  bad , 2.  poor , 3.  fair , 4.  good , and 5.  excellent . Subsequent to the naturalness evaluation, participants were prompted to identify one of the six distinct emotions with which the speech sample was narrated.

We compiled an evaluation set of 3,600 audio samples that were not included in the training set, from which a random subset of 36 (18 ground truth and 18 synthesized) speech samples was presented to each participant. The samples were selected to ensure an equal representation of each narrator and emotion, amounting to six samples per narrator and one per emotion. Participants were presented with one speech sample at a time. While participants were afforded the opportunity to listen to each sample multiple times, it was emphasized that their selection could not be altered once submitted.

5.   Results and Discussion

The evaluation results are provided in Tables  3 –  5 . As can be seen from Table  3 , on average, the synthesized speech delivered in a female voice demonstrated a greater likeness to the corresponding ground truth samples compared to the synthesized samples in both male voices. An interesting observation is that synthesized speech samples featuring emotional states typically associated with lower-pitched voices (e.g., neutral, sad, scared) exhibited greater similarity to the corresponding ground truth samples. Conversely, speech samples generated to convey emotional states characterized by higher-pitched voices (e.g., angry, happy, surprised) demonstrated a comparatively lower degree of similarity to the ground truth samples.

As for the evaluation survey, there were a total of 64 participants. The \ac mos for assessing the naturalness of the synthesized speech varied only slightly, ranging from 3.51 to 3.57 (see Table  3 ). Narrator M2’s samples attained the highest \acp mos for both the ground truth and synthesized samples, with M2’s synthesized samples achieving a slightly higher score than that of F1 by a margin of 0.02. The generated speech of Narrator M1 received the lowest \ac mos. This underscores the lack of a correlation between \acs mos and the volume of data accessible for each narrator. Despite the greater volume of data for Narrator F1 in comparison to the other two narrators, it did not translate into a much higher \ac mos for the female narrator. Similarly, the \ac mos was not higher for Narrator M1, despite having nearly twice as many data as Narrator M2.

A comparison of \acp mos highlights notably higher results in a separate study focused on Kazakh \ac tts  (Mussakhojayeva et al., 2022 ) . For female speakers, ground truth speech evaluations achieved scores within the range of 4.18 to 4.73, while \acp mos for the generated speech covered a spectrum from 4.05 to 4.53. In the case of male speakers, \acp mos for ground truth ranged from 4.37 to 4.43, while scores for synthesized speech spanned from 3.95 to 4.2.

In English emotional \ac tts studies, results closely aligned with our findings were reported by Zhou et al. ( 2022a ) . They achieved a \ac mos of 3.45 when the emotion “surprised” was presented with 0% intensity of other emotions. Notably, superior performance was observed with EmoDiff  (Guo et al., 2022a ) , scoring 4.01, and EmoMix  (Tang et al., 2023 ) , which attained a \ac mos of 3.92.

As illustrated in Table  4 , sentences delivered in a neutral manner were more accurately recognized as such, achieving an accuracy rate of 65%. In contrast, sentences expressed with anger proved to be the most challenging to identify, with a recognition accuracy of only 22%.

Table  5 displays the percentages of participant responses regarding their choice of emotion and reveals that “neutral” was frequently selected by participants when identifying the emotion of a speech sample. Interestingly, “happy” was the most easily identifiable emotion, chosen by nearly half of all participants, irrespective of the narrator. It is also worth noting that participants faced challenges when distinguishing “angry” speech samples, often mistaking them for “sad” or “scared” expressions. Frequently, samples labelled as “scared” were also erroneously identified as “sad”.

In light of the \ac mos results acquired, it is apparent that despite providing participants with explicit instructions to focus on the emotion conveyed by the delivery of a sentence, rather than its inherent message or content, we acknowledge the cognitive challenge of perceiving a sentence with inherently somber content, such as one related to a grave illness or loss of life, being articulated with a seemingly cheerful emotion. This presents a cognitive challenge that may not have been fully resolved in our current study. In our future emotional \ac tts studies, we aim to address this challenge more effectively.

6.   Conclusion

This study aimed to construct the KazEmoTTS dataset for Kazakh emotional \ac tts applications. The dataset comprises a substantial 54,760 audio-text pairs, covering a total duration of 74.85 hours. This includes 34.23 hours delivered by a female narrator and 40.62 hours by two male narrators. The emotional spectrum within the dataset covers “neutral”, “angry”, “happy”, “sad”, “scared”, and “surprised” states. In addition, a \ac tts model was developed through training on the KazEmoTTS dataset. Both objective and subjective evaluations were performed to gauge the synthesized speech quality, resulting in an objective \ac mcd metric ranging from 6.02 to 7.67 and an \ac mos ranging from 3.51 to 3.57. Our findings are particularly promising, considering that this study represents the first attempt at emotional \ac tts for Kazakh. To facilitate replicability and further exploration, we have made our code, pre-trained model, and dataset available in our GitHub repository. 1

7.   Acknowledgements

We would like to express our gratitude to the narrators for their contributions and to the anonymous raters for their valuable evaluations.

8.   Bibliographical References

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$39 million for major expansion of Child Development Service

• $39 million boost to Child Development Service (CDS) amid unprecedent demand
• Will allow for rapid expansion of services and significantly increase workforce
• Part of Cook Government's ongoing commitment to improving CDS for WA kids

The Child Development Service (CDS) will be significantly expanded, with the Cook Government today announcing $39 million to substantially increase staff and overhaul the vital service.

The Cook Government will invest $39 million in both the Child and Adolescent Health Service metropolitan service (CAHS-CDS) and WA Country Health Service regional service (WACHS-CDS), as part of the 2024-25 State Budget. 

The funding will facilitate a significant increase in clinical staff, including paediatricians, clinical nurse specialists, speech pathologists, occupational therapists, psychologists and audiologists, in both the metro and regional areas.

The $39 million boost also includes infrastructure funding to lease additional temporary accommodation to allow staff to see more families.

The CDS is the only public child development service in Australia where both assessment and intervention services are provided by a multidisciplinary team of paediatricians and allied health clinicians under the one service umbrella.

The Service has experienced an unprecedented surge in demand at a time when there is a worldwide shortage of paediatricians. In the past 10 years referrals to CDS paediatricians have risen by 132 per cent.

The substantial funding boost complements other measures already being undertaken by the Cook Government to support the CDS, including:

• piloting a joint nurse/paediatrician medication review process;          
• piloting combined planning and assessment appointments for suitable patient cohorts;
• changing the Schedule 8 Prescribing Code to allow approved specialists from interstate to prescribe certain medicines to Western Australian patients via telehealth, removing a barrier for patients;
• operating on Saturdays at some sites;
• transitioning to an electronic referrals process;
• embarking on a refreshed recruitment drive;
• investigating ways to provide better linkages between the CDS and General Practice; and
• empowering clinical nurse specialists to work to their full scope of practise to speed up initial access to care for children with suspected ADHD.

Comments attributed to Health Minister Amber-Jade Sanderson:

"There is no other service in the country that does what the CDS does. Our service looks after kids across 2.5 million square kilometres – from Kununurra to Albany – and the staff working in this service do a great job.

"Demand growth for CDS has been far exceeding what you would expect for our population, with referrals growing at an unprecedented rate.

"This $39 million investment will pave the way for a major uplift in CDS staff, especially in clinical roles, and ensures this vital service meets the needs of WA kids and families."

Hon. Amber-Jade Sanderson

Acknowledgement of country.

The Government of Western Australia acknowledges the traditional custodians throughout Western Australia and their continuing connection to the land, waters and community. We pay our respects to all members of the Aboriginal communities and their cultures; and to Elders both past and present.