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.

A consolidated description of my current reseach activities, alongwith my publications is now at the Center for Voice Intelligence and Security.

I began working in the area of computer speech recognition and general audio processing in 1997. Until 2014, 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 of information from the voice signal, guided by the intricacies of the physics and bio-mechanics of human voice production. Because it focuses on the voice signal, and not its semantic or pragmatic content, it is agnostic to language.

Currently my work includes the design of powerful AI systems to explore the depths of information in the human voice. Examples include systems for genetic discovery, systems for biomarker discovery and systems for other kinds of explorations into the human physical state and psyche, including emotions and personality, through the portal of voice.

In addition to my work on human profiling, I am working on creating core designs for general AI systems that are capable of universal speech and audio processing. The goal of this endeavor is to eventually build a system that is capable of all that our brain is capable of doing, in response to multisensory input from the world. This will require powerful computing. And then we will need to give it mobility. I have begun working on these aspects as well.

Media coverage

More about this work

My latest presentations

Book coming up online: Deep Learning

Book chapters are here

This book is being written in tandem with the CMU graduate level course: Introduction to Deep Learning, taught by Prof. Bhiksha Raj. The book is an accompaniment to this course.

Courses 2024

  1. Introduction to Deep Learning, Spring 2024

  2. Quantum Computing, Cryptography and Machine Learning Lab Spring 2024 Website
    s3 cmu

Current Students

A full list is available here

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

Recent Research Publications

A full list is available here.

  • A Gene-Based Algorithm for Identifying Factors That May Affect a Speaker's Voice, Rita Singh, Entropy 25, No. 6: 897. 2023. pdf

    Some Older Publications

  • 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

  • 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 and Art

    Some more publications (much older, by topic)

    Note: Most older papers that made a difference back then, are now obsolete. I have removed them from my Google Scholar Page , which I use for my own quick reference for tracking a few current papers. The list below contains some older papers.

    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!)
    • Sphinx-4
    • LDC And other things for me...

    Earlier Teaching

    (Graduate level courses)

    1. Introduction to Deep Learning, Fall and Spring: 2020, 2021, 2022, 2023 , Website
      Co -instructor. I am writing this Book in tandem with the course for students to read.

    2. Artificial Intelligence in Digital Multimedia and Cyber Forensics, Fall 2023 at the University of Pittsburgh.

    3. Concepts in Digital Multimedia and Cyber Forensics, Spring 2022, Website

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

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

    6. Advnaced Topics: Quantum Computing Theory and Lab, Spring 2022, Website

    7. Generative AI for Software Implementations in Quantum Computing and Machine Learning Summer 2023.

    8. Quantum Machine Learning Fall 2023, Website

    9. Large-Scale Multimedia Analysis ( 2 versions: grad level and exec-ed), Spring 2020, Spring 2021, Spring 2022, Website

    10. 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
    11. 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.
    12. Design and Implementation of Speech Recognition Systems
      Last taught many years ago. Earliest version co-taught with Prof. James Karl 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     My very brief travel log