RESEARCH INTERESTS I work on core algorithmic aspects of computer voice recognition, and artificial intelligence applied to voice forensics. My focus is on the development of technology for the automated discovery, measurement, representation and learning of the information encoded in voice signal for optimal voice intelligence.

I worked on computer speech recognition and general audio processing from 1997 to 2014. During that time, I worked on a wide range of topics, including algorithms that made speech processing systems completely generalizable (agnostic to language), algorithms that enabled automated discovery and learning of information from speech, algorithms that could process speech using minimal external (human-generated) knowledge etc. My goal was to enable greater automation, create more powerful search strategies and more scaleable learning algorithms for voice processing systems, and to find ways to make them work more accurately in high-noise and other kinds of complex acoustic environments.

In December 2014, I began building up the science of profiling humans from their voice. This involves the concurrent deduction of myriad human parameters from voice. Like the DNA and fingerprints, every human's voice is unique. It carries more information than we realize (or can hear). It carries signatures of the speaker's physical, physiological, medical, psychological, sociological, behavioral and environmental parameters, among other things. Profiling is based on quantitative discovery and measurement of micro-features from the voice signal, and the intricacies of the physics and bio-mechanics of human voice production. Because it focuses on the voice signal, and not its pragmatic content, it is agnostic to language.

Media coverage

More about this work

My latest presentations


My most recent talk about it is here

Courses Spring 2022

( s3 cmu )

  1. Computational Forensics and AI (I will likely cancel this)

  2. Advanced Topics: Quantum Computing Lab Website

  3. Large-Scale Multimedia Processing (Website coming up. Two versions will be taught: Exec-ed and grad level)



  • Yolanda Gao, PhD, Electrical and Computer Engineering
  • Wayne Zhao, PhD, Electrical and Computer Engineering
  • Yandong Wen, PhD, Electrical and Computer Engineering Website
  • Shahan Ali Memon, Masters, LTI, School of Computer Science Website
  • Hira Yasin, Masters, LTI, School of Computer Science
  • Mahmoud Al Ismail, Masters, LTI, School of Computer Science

Technical Publications: Books

 Profiling Humans from their Voice
Profiling Humans from their Voice
Rita Singh
First published: July 2019
Publisher: Springer, Singapore
Copyright 2019 Springer-Nature, Switzerland, July 2019
ISBN: ISBN 978-981-13-8402-8
Also available on, other bookstores and ebay.
Chapters of this book are separately available from Springer. Click this link to see the list.
 Techniques for Noise Robustness in Automatic Speech Recognition
Techniques for Noise Robustness in Automatic Speech Recognition
Tuomas Virtanen, Rita Singh, Bhiksha Raj (Eds)
First published:5 October 2012
Copyright 2013 John Wiley & Sons, Ltd
Print ISBN:9781119970880 |Online ISBN:9781118392683 |DOI:10.1002/9781118392683

Research Publications

Recent papers on voice profiling

  • Detection of COVID-19 through the analysis of vocal fold oscillations, Mahmoud Al Ismail, Soham Deshmukh, Rita Singh, arXiv:2010.10707, pdf

  • Interpreting glottal flow dynamics for detecting COVID-19 from voice, Soham Deshmukh, Mahmoud Al Ismail, Rita Singh, arXiv:2010.10707, pdf

  • Speech-based parameter estimation of an asymmetric vocal fold oscillation model and its application in discriminating vocal fold pathologies, Wenbo Zhao, Rita Singh, Int. conf. on Acoustics, Speech and Signal Processing (ICASSP), April 2020. pdf (nominated for the best student paper award at ICASSP 2020)

  • Hierarchical Routing Mixture of Experts, Wenbo Zhao, Yang Gao, Shahan Ali Memon, Bhiksha Raj, Rita Singh, 2020 25th International Conference on Pattern Recognition (ICPR), 2020.   pdf

  • Optimizing neural network embeddings using a pair-wise loss for text-independent speaker verification Hira Dhamyal, Tianyan Zhou, Bhiksha Raj, and Rita Singh, IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), pp. 742-748. IEEE, 2019. pdf

  • Face reconstruction from voice using generative adversarial networks, Yandong Wen, Bhiksha Raj, Rita Singh, Advances in Neural Information Processing Systems (NEURIPS 2019), 2019, pp. 7344-7348   pdf (created a social media and media furore over totally unrelated transgender issues...)

  • Disjoint mapping network for cross-modal matching of voices and faces, Yandong Wen, Mahmoud Al Ismail, Wenbo Liu, Bhiksha Raj, Rita Singh, International Conference on Learning Representations (ICLR), 2019.   pdf

  • Detecting gender differences in perception of emotion in crowdsourced data, Shahan Ali Memon, Hira Dhamyal, Oren Wright, Daniel Justice, Vijaykumar Palat, William Boler, Bhiksha Raj, Rita Singh (arXiv:1910.11386), 2020. pdf

  • Neural Regression Trees, Shahan Ali Memon, Wenbo Zhao, Bhiksha Raj, Rita Singh, (IJCNN), 2019. pdf

  • The phonetic bases of vocal expressed emotion: natural versus acted, Hira Dhamyal, Shahan Ali Memon, Bhiksha Raj, Rita Singh (INTERSPEECH), 2020. pdf

  • list to be updated..........

  • Voice impersonation using generative adversarial networks, Yang Gao, Rita Singh, Bhiksha Raj, Int. conf. on Acoustics, Speech and Signal Processing (ICASSP),Calgary, Canada, 15-20 April 2018 Canada. pdf

  • A corrective training approach for text-independent speaker verification, Yandong Wen, Tianyan Zhou, Rita Singh, Bhiksha Raj, Int. conf. on Acoustics, Speech and Signal Processing (ICASSP),Calgary, Canada, 15-20 April 2018 Canada. pdf

  • Voice disguise by mimicry: deriving statistical articulometric evidence to evaluate claimed impersonation, Rita Singh, Abelino Jiminez and Anders Oland, IET Biometrics, January 2017. pdf

  • more below....

    Literary creations

    Research publications (by topic)

    1. Forensics    Papers
      General theme: Forensic deductions from human voice. Speech and audio forenics are included.

    2. General audio analysis, microphone array processing, denoising, dereverberation, signal restoration    Papers
      General theme: Our approach is that of modeling the effect of highly-nonstationary noise and reverberation as compositional phenomena. Clean signals can then be recomposed from the bases of the composition. This approach differs from ones that model audio phenomena using dynamic generative models.

    3. Semi-supervised learning, structure discovery, statistical pattern recognition, classification    Papers
      These papers cover diferent topics such as learning basic units of sound from data, discovering pronunciations for words in terms of these units, selecting better classifiers using weaker classifiers iteratively in a gradient ascent solution to training good acoustic models from completely untranscribed data etc.. They also include general developments in classification techniques.

    4. Acoustic modeling, decoding, speech processing, speech recognition, adaptation, keyword spotting    Papers
      These papers relate to core and peripheral issues in speech recognition and processing for HMM-based ASR systems.

    5. Systems, applications, projects    Papers
      These papers describe systems developed or deployed for specific tasks. Also include papers from short-term student projects, technical reports and other writeups

    6. Miscellaneous    Papers
      Patents, papers on other topics such as chaos theory, radar signal design, geodynamics. From 1993-1998 I worked on these topics. Chaos and complexity theory remain my favorite hobby subjects.

    Other activities

    • Associate Editor, IEEE Signal Processing Letters (Retired recently!)
    • Sphinx-4
    • LDC And other things for me...

    Earlier Teaching

    1. Introduction to Deep Learning
      Fall 2020, Spring 2021, Fall 2021, Website
      Co -instructor. I am writing this Book in tandem with the course for students to read.

    2. Computational Forensics and AI
      Spring 2020, Spring 2021, Website)

    3. Advanced Topics: Quantum Computing Lab
      Spring 2020, Spring 2021, Website

    4. Large-Scale Multimedia Processing ( 2 versions: grad level and exec-ed)
      Spring 2020, Spring 2021, Website

    5. Computational Forensics and Investigative Intelligence
      Taught in Spring 2017 and Spring 2018, simultaneously at
      • CMU Pittsburgh
      • Hamad Bin Khalifa University (HBKU), Qatar
      • CMU Qatar
      • CMU Africa
      • Syllabus
    6. An Introduction to Knowledge based Deep Learning and Socratic Coaches
      11-364 CMU Pittsburgh
      This course was taught in person by Prof. James Karl Baker at the CMU Pittsburgh location. I was nominally co-instructor but couldn't help Jim much.
    7. Design and Implementation of Speech Recognition Systems
      Last taught many years ago. Earliest version co-taught with Prof. James Baker

    About me: I'm happiest where I come from. I like simple things. I admire art. When I have time I spend much of it looking at art. I write poetry. I collect comics (the Harvey Pekar and Blake and Mortimer kind..) and puzzles (the Charles Wysocki and Jane Wooster Scott kind..). I read mysteries. I don't watch TV or movies, I haven't switched on my TV for years. I dont know if my TV works. I don't use a cellphone, I have one but its mostly lost anyway. I'd rather watch the clouds in the sky, and the birds and the leaves. A groundhog lives in a grand home under the deck stairs just outside my window. It even has a solar-powered lamp outside its home. I can tell you all about its likes and dislikes, habits, friends and daily routine. In the summer I wake up to the song of the cardinal. I want nothing more from life or the world, except for medical science to hurry up and make everyone well. Other than that, I am content.

    Some hi_res pictures of me