dissertation on speech recognition

End-to-End Speech Recognition Models

For the past few decades, the bane of Automatic Speech Recognition (ASR) systems have been phonemes and Hidden Markov Models (HMMs). HMMs assume conditional indepen-dence between observations, and the reliance on explicit phonetic representations requires expensive handcrafted pronunciation dictionaries. Learning is often via detached proxy problems, and there especially exists a disconnect between acoustic model performance and actual speech recognition performance. Connectionist Temporal Classification (CTC) character models were recently proposed attempts to solve some of these issues, namely jointly learning the pronunciation model and acoustic model. However, HMM and CTC models still suffer from conditional independence assumptions and must rely heavily on language models during decoding. In this thesis, we question the traditional paradigm of ASR and highlight the limitations of HMM and CTC models. We propose a novel approach to ASR with neural attention models and we directly optimize speech transcriptions. Our proposed method is not only an end-to- end trained system but also an end-to-end model. The end-to-end model jointly learns all the traditional components of a speech recognition system: the pronunciation model, acoustic model and language model. Our model can directly emit English/Chinese characters or even word pieces given the audio signal. There is no need for explicit phonetic representations, intermediate heuristic loss functions or conditional independence assumptions. We demonstrate our end-to-end speech recognition model on various ASR tasks. We show competitive results compared to a state-of-the-art HMM based system on the Google voice search task. We demonstrate an online end-to-end Chinese Mandarin model and show how to jointly optimize the Pinyin transcriptions during training. Finally, we also show state-of-the-art results on the Wall Street Journal ASR task compared to other end-to-end models.

Degree Type

• Dissertation
• Electrical and Computer Engineering

Degree Name

• Doctor of Philosophy (PhD)

Usage metrics

• Computer Engineering
• Electrical and Electronic Engineering not elsewhere classified

2011 Theses Doctoral

Automatic Dialect and Accent Recognition and its Application to Speech Recognition

Biadsy, Fadi

A fundamental challenge for current research on speech science and technology is understanding and modeling individual variation in spoken language. Individuals have their own speaking styles, depending on many factors, such as their dialect and accent as well as their socioeconomic background. These individual differences typically introduce modeling difficulties for large-scale speaker-independent systems designed to process input from any variant of a given language. This dissertation focuses on automatically identifying the dialect or accent of a speaker given a sample of their speech, and demonstrates how such a technology can be employed to improve Automatic Speech Recognition (ASR). In this thesis, we describe a variety of approaches that make use of multiple streams of information in the acoustic signal to build a system that recognizes the regional dialect and accent of a speaker. In particular, we examine frame-based acoustic, phonetic, and phonotactic features, as well as high-level prosodic features, comparing generative and discriminative modeling techniques. We first analyze the effectiveness of approaches to language identification that have been successfully employed by that community, applying them here to dialect identification. We next show how we can improve upon these techniques. Finally, we introduce several novel modeling approaches -- Discriminative Phonotactics and kernel-based methods. We test our best performing approach on four broad Arabic dialects, ten Arabic sub-dialects, American English vs. Indian English accents, American English Southern vs. Non-Southern, American dialects at the state level plus Canada, and three Portuguese dialects. Our experiments demonstrate that our novel approach, which relies on the hypothesis that certain phones are realized differently across dialects, achieves new state-of-the-art performance on most dialect recognition tasks. This approach achieves an Equal Error Rate (EER) of 4% for four broad Arabic dialects, an EER of 6.3% for American vs. Indian English accents, 14.6% for American English Southern vs. Non-Southern dialects, and 7.9% for three Portuguese dialects. Our framework can also be used to automatically extract linguistic knowledge, specifically the context-dependent phonetic cues that may distinguish one dialect form another. We illustrate the efficacy of our approach by demonstrating the correlation of our results with geographical proximity of the various dialects. As a final measure of the utility of our studies, we also show that, it is possible to improve ASR. Employing our dialect identification system prior to ASR to identify the Levantine Arabic dialect in mixed speech of a variety of dialects allows us to optimize the engine's language model and use Levantine-specific acoustic models where appropriate. This procedure improves the Word Error Rate (WER) for Levantine by 4.6% absolute; 9.3% relative. In addition, we demonstrate in this thesis that, using a linguistically-motivated pronunciation modeling approach, we can improve the WER of a state-of-the art ASR system by 2.2% absolute and 11.5% relative WER on Modern Standard Arabic.

• Computer science

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Machine listening: Making speech recognition systems more inclusive

Study explores how african american english speakers adapt their speech to be understood by voice technology..

Interactions with voice technology, such as Amazon's Alexa, Apple's Siri, and Google Assistant, can make life easier by increasing efficiency and productivity. However, errors in generating and understanding speech during interactions are common. When using these devices, speakers often style-shift their speech from their normal patterns into a louder and slower register, called technology-directed speech.

Research on technology-directed speech typically focuses on mainstream varieties of U.S. English without considering speaker groups that are more consistently misunderstood by technology. In JASA Express Letters, published on behalf of the Acoustical Society of America by AIP Publishing, researchers from Google Research, the University of California, Davis, and Stanford University wanted to address this gap.

One group commonly misunderstood by voice technology are individuals who speak African American English, or AAE. Since the rate of automatic speech recognition errors can be higher for AAE speakers, downstream effects of linguistic discrimination in technology may result.

"Across all automatic speech recognition systems, four out of every ten words spoken by Black men were being transcribed incorrectly," said co-author Zion Mengesha. "This affects fairness for African American English speakers in every institution using voice technology, including health care and employment."

"We saw an opportunity to better understand this problem by talking to Black users and understanding their emotional, behavioral, and linguistic responses when engaging with voice technology," said co-author Courtney Heldreth.

The team designed an experiment to test how AAE speakers adapt their speech when imagining talking to a voice assistant, compared to talking to a friend, family member, or stranger. The study tested familiar human, unfamiliar human, and voice assistant-directed speech conditions by comparing speech rate and pitch variation. Study participants included 19 adults identifying as Black or African American who had experienced issues with voice technology. Each participant asked a series of questions to a voice assistant. The same questions were repeated as if speaking to a familiar person and, again, to a stranger. Each question was recorded for a total of 153 recordings.

Analysis of the recordings showed that the speakers exhibited two consistent adjustments when they were talking to voice technology compared to talking to another person: a slower rate of speech with less pitch variation (more monotone speech).

"These findings suggest that people have mental models of how to talk to technology," said co-author Michelle Cohn. "A set 'mode' that they engage to be better understood, in light of disparities in speech recognition systems."

There are other groups misunderstood by voice technology, such as second-language speakers. The researchers hope to expand the language varieties explored in human-computer interaction experiments and address barriers in technology so that it can support everyone who wants to use it.

• Communications
• Artificial Intelligence
• Information Technology
• Computer Science
• STEM Education
• Racial Disparity
• Media and Entertainment
• Voice over IP
• Speech recognition
• Consumerism
• Civil libertarianism
• Communication
• Social inequality
• Virtual reality

Story Source:

Materials provided by American Institute of Physics . Note: Content may be edited for style and length.

Journal Reference :

• Michelle Cohn, Zion Mengesha, Michal Lahav, Courtney Heldreth. African American English speakers’ pitch variation and rate adjustments for imagined technological and human addressees . JASA Express Letters , 2024; 4 (4) DOI: 10.1121/10.0025484

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Dissertations / Theses on the topic 'Speech emotion recognition'

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Sidorova, Julia. "Optimization techniques for speech emotion recognition." Doctoral thesis, Universitat Pompeu Fabra, 2009. http://hdl.handle.net/10803/7575.

Pachoud, Samuel. "Audio-visual speech and emotion recognition." Thesis, Queen Mary, University of London, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.528923.

Iliev, Alexander Iliev. "Emotion Recognition Using Glottal and Prosodic Features." Scholarly Repository, 2009. http://scholarlyrepository.miami.edu/oa_dissertations/515.

Väyrynen, E. (Eero). "Emotion recognition from speech using prosodic features." Doctoral thesis, Oulun yliopisto, 2014. http://urn.fi/urn:isbn:9789526204048.

Ma, Rui. "Parametric Speech Emotion Recognition Using Neural Network." Thesis, Högskolan i Gävle, Avdelningen för elektronik, matematik och naturvetenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-17694.

Rintala, Jonathan. "Speech Emotion Recognition from Raw Audio using Deep Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278858.

Mancini, Eleonora. "Disruptive Situations Detection on Public Transports through Speech Emotion Recognition." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24721/.

Al-Talabani, Abdulbasit. "Automatic Speech Emotion Recognition : feature space dimensionality and classification challenges." Thesis, University of Buckingham, 2015. http://bear.buckingham.ac.uk/101/.

Sun, Rui. "The evaluation of the stability of acoustic features in affective conveyance across multiple emotional databases." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49041.

Noé, Paul-Gauthier. "Emotion Recognition in Football Commentator Speech : Is the action intense or not ?" Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-289370.

Tinnemore, Anna, and Anna Tinnemore. "Improving Understanding of Emotional Speech Acoustic Content." Diss., The University of Arizona, 2017. http://hdl.handle.net/10150/625368.

Bhullar, Naureen. "Effects of Facial and Vocal Emotion on Word Recognition in 11-to-13-month-old infants." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/27502.

Nguyen, Tien Dung. "Multimodal emotion recognition using deep learning techniques." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/180753/1/Tien%20Dung_Nguyen_Thesis.pdf.

Siddiqui, Mohammad Faridul Haque. "A Multi-modal Emotion Recognition Framework Through The Fusion Of Speech With Visible And Infrared Images." University of Toledo / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1556459232937498.

Acosta, Jaime Cesar. "Using emotion to gain rapport in a spoken dialog system." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2009. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

Pon-Barry, Heather Roberta. "Inferring Speaker Affect in Spoken Natural Language Communication." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10710.

Deng, Jun [Verfasser], Björn W. [Akademischer Betreuer] [Gutachter] Schuller, and Werner [Gutachter] Hemmert. "Feature Transfer Learning for Speech Emotion Recognition / Jun Deng. Betreuer: Björn W. Schuller. Gutachter: Björn W. Schuller ; Werner Hemmert." München : Universitätsbibliothek der TU München, 2016. http://d-nb.info/1106382331/34.

Iriya, Rafael. "Análise de sinais de voz para reconhecimento de emoções." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/3/3142/tde-14042015-160249/.

Chandrapati, Srivardhan. "Multi-modal expression recognition." Thesis, Manhattan, Kan. : Kansas State University, 2008. http://hdl.handle.net/2097/762.

KHALIFA, INTISSAR. "Deep psychology recognition based on automatic analysis of non-verbal behaviors." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2021. http://hdl.handle.net/10281/314920.

Guerrero, Razuri Javier Francisco. "Decisional-Emotional Support System for a Synthetic Agent : Influence of Emotions in Decision-Making Toward the Participation of Automata in Society." Doctoral thesis, Stockholms universitet, Institutionen för data- och systemvetenskap, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-122084.

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 8: Accepted.

Žukas, Gediminas. "Kalbos emocijų požymių tyrimas." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2014~D_20140617_133242-89394.

Vlasenko, Andrej. "Studentų emocinės būklės testavimo metu tyrimas panauduojant biometrines technologijas." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2012. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2012~D_20120329_153219-37955.

Zhu, Winstead Xingran. "Hotspot Detection for Automatic Podcast Trailer Generation." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-444887.

Navrátil, Michal. "Rozpoznávání emočních stavů pomocí analýzy řečového signálu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-217263.

Pfeifer, Leon. "Automatické rozpoznávání emočních stavů člověka na základě analýzy řečového projevu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-217520.

Shaukat, Arslan. "Automatic Emotional State Analysis and Recognition from Speech Signals." Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.511910.

Atassi, Hicham. "Rozpoznání emočního stavu z hrané a spontánní řeči." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2014. http://www.nusl.cz/ntk/nusl-233665.

Hansson, Svan Angus, and Carl Mannerstråle. "Prediktion av användaromdömen om språkcafé-samtal baserat på automatisk röstanalys." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-261639.

Planet, García Santiago. "Reconeixement afectiu automàtic mitjançant l'anàlisi de paràmetres acústics i lingüístics de la parla espontània." Doctoral thesis, Universitat Ramon Llull, 2013. http://hdl.handle.net/10803/125335.

Ferro, Adelino Rafael Mendes. "Speech emotion recognition through statistical classification." Master's thesis, 2017. http://hdl.handle.net/10400.14/22817.

"Optimization techniques for speech emotion recognition." Universitat Pompeu Fabra, 2009. http://www.tesisenxarxa.net/TDX-0113110-133822/.

Yeh, Jun-Heng, and 葉俊亨. "Emotion Recognition from Mandarin Speech Signals." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/2f4evr.

Chiou, Bo-Chang, and 邱柏菖. "Cross-Lingual Automatic Speech Emotion Recognition." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/23736438894309347506.

SHEN, MENG-JHEN, and 沈孟蓁. "Research on Speech Emotion Recognition Systems." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/j5m53v.

CHENG, KUAN-JUNG, and 程冠融. "Cross-Lingual Speech Emotion Recognition Based on Speech Recognition Technology in An Emotional Speech Database in Mandarin, Taiwanese, and Hakka." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/6c4m2x.

Wang, Chun-Ming, and 王俊明. "Speech Emotion Recognition using 2D texture features." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/6y77cs.

Su, Yu-Che, and 蘇于哲. "Emotion Recognition based on Chinese Speech Signals." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/54453655022687274699.

Li, Pei-jia, and 李珮嘉. "Emotion Recognition from Continuous Mandarin Speech Signal." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/79746323884839442339.

Bakhshi, Ali. "Speech emotion recognition using deep neural networks." Thesis, 2021. http://hdl.handle.net/1959.13/1430839.

Yeh, Lan-Ying, and 葉藍霙. "Spectro-Temporal Modulations for Robust Speech Emotion Recognition." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/56883607879338166423.

Wu, Chien-Feng, and 吳鑑峰. "Bimodal Emotion Recognition from Speech and Facial Expression." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/g8tuye.

"Emotion Recognition and Traumatic Brain Injury." Master's thesis, 2011. http://hdl.handle.net/2286/R.I.9087.

Huang, Ching-Hsiu, and 黃慶修. "Emotion recognition of spontaneous speech using mutiple-instance learning." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/21389130030838831132.

Hsu, Jin-Huai, and 許晉懷. "Bimodal Emotion Recognition System Using Image and Speech Information." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/75013414815783421468.

Chen, Chia-ying, and 陳嘉穎. "Speech Emotion Recognition Using Factor Analysis and Identity Vectors." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/14016869692695939636.

Yang, Bo-Cheng, and 楊博丞. "Adversarial Feature Augmentation for Cross-corpus Speech Emotion Recognition." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/hrvxvp.

Manamela, Phuti John. "The automatic recognition of emotions in speech." Thesis, 2020. http://hdl.handle.net/10386/3347.

Lin, Ching-yi, and 林靜宜. "A Study on Identifying the Most Effective Speech Features for Speech Emotion Recognition." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/77453967950782385836.

Vogt, Thurid [Verfasser]. "Real-time automatic emotion recognition from speech / von Thurid Vogt." 2010. http://d-nb.info/1010495038/34.

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Spring 2024 Recognition Ceremony Program

Congratulations to the electrical and computer engineering Class of 2024!

And a special thank you to all of the friends and family who have supported our graduates during their time at CU Boulder. 

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Processional

Welcoming Remarks Professor Chris Myers, Department Chair and Palmer Leadership Chair in Electrical, Computer & Energy Engineering

Acknowledgement of Award Winners

Holland Teaching Award Faculty Keynote Assistant Professor Joshua Combes​

Undergraduate Student Address Jasleen Batra

Master's Student Address Gabriel Altman

PhD Student Address Neeraj Prakash

Presentation of the PhD Candidates  Professor Sean Shaheen, Associate Chair for Research and Graduate Education

Presentation of Master’s Candidates Professor Sean Shaheen, Associate Chair for Research and Graduate Education

Presentation of BS Candidates Associate Teaching Professor Mona ElHelbawy, Associate Chair for Undergraduate Education

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Reception Please join us to continue celebrating our graduates. Light refreshments will be provided. 

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Microsoft bans US police departments from using enterprise AI tool for facial recognition

Microsoft has reaffirmed its ban on U.S. police departments from using generative AI for facial recognition through Azure OpenAI Service , the company’s fully managed, enterprise-focused wrapper around OpenAI tech.

Language added Wednesday to the terms of service for Azure OpenAI Service more clearly prohibits integrations with Azure OpenAI Service from being used “by or for” police departments for facial recognition in the U.S., including integrations with OpenAI’s current — and possibly future — image-analyzing models.

A separate new bullet point covers “any law enforcement globally,” and explicitly bars the use of “real-time facial recognition technology” on mobile cameras, like body cameras and dashcams, to attempt to identify a person in “uncontrolled, in-the-wild” environments.

The changes in policy come a week after Axon, a maker of tech and weapons products for military and law enforcement, announced a new product that leverages OpenAI’s GPT-4 generative text model to summarize audio from body cameras. Critics were quick to point out the potential pitfalls, like hallucinations (even the best generative AI models today invent facts) and racial biases introduced from the training data (which is especially concerning given that people of color are far more likely to be stopped by police than their white peers).

It’s unclear whether Axon was using GPT-4 via Azure OpenAI Service, and, if so, whether the updated policy was in response to Axon’s product launch. OpenAI had previously restricted the use of its models for facial recognition through its APIs. We’ve reached out to Axon, Microsoft and OpenAI and will update this post if we hear back.

The new terms leave wiggle room for Microsoft.

The complete ban on Azure OpenAI Service usage pertains only to U.S. , not international, police. And it doesn’t cover facial recognition performed with stationary cameras in controlled environments, like a back office (although the terms prohibit any use of facial recognition by U.S. police).

That tracks with Microsoft’s and close partner OpenAI’s recent approach to AI-related law enforcement and defense contracts.

In January, reporting by Bloomberg revealed that OpenAI is working with the Pentagon on a number of projects including cybersecurity capabilities — a departure from the startup’s earlier ban on providing its AI to militaries. Elsewhere, Microsoft has pitched using OpenAI’s image generation tool, DALL-E, to help the Department of Defense (DoD) build software to execute military operations, per The Intercept.

Azure OpenAI Service became available in Microsoft’s Azure Government product in February, adding additional compliance and management features geared toward government agencies, including law enforcement. In a blog post , Candice Ling, SVP of Microsoft’s government-focused division Microsoft Federal, pledged that Azure OpenAI Service would be “submitted for additional authorization” to the DoD for workloads supporting DoD missions.

Update: After publication, Microsoft said its original change to the terms of service contained an error, and in fact the ban applies only to facial recognition in the U.S. It is not a blanket ban on police departments using the service.