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
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 2022
( s3 cmu )
- Concepts in Digital Multimedia and Cyber Forensics (Spring)
Old name: Computational Forensics and AI
Website
- Advanced Topics: Quantum Computing Theory and Lab (Spring)
Website
- Large-Scale Multimedia Analysis
Website (Two versions in Spring, One in Fall coming up...)
Current Students
- Yolanda Gao, PhD, Completed May 2022 (Currently at Amazon Inc.)
- Wayne Zhao, PhD, Expected July 2022 (Currently at Amazon Inc.)
- Yandong Wen, PhD, Completed May 2022 (Currently at Max Planck Institute, Germany) Website
- Shahan Ali Memon Website
- Hira Yasin, PhD, Ongoing, School of Computer Science
- Ankit Parag Shah, PhD, Ongoing, School of Computer Science
- Mahmoud Al Ismail, Deferred (Currently at Microsoft Corp.), School of Computer Science
Technical Publications: Books
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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 springer.com, other bookstores and ebay. Chapters of this book are separately available from Springer. Click this link to see the list. |
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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
more below....
Literary creations and Art
Books: Click here for details
Art: My paintings
Research publications (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.
- Forensics
Papers
General theme: Forensic deductions from human voice. Speech and audio forenics are included. - 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. - 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. - 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. - 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 - 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
- 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. - Computational Forensics and AI
Spring 2020, Spring 2021, Website) - Advanced Topics: Quantum Computing Lab
Spring 2020, Spring 2021, Website - Large-Scale Multimedia Processing ( 2 versions: grad level and exec-ed)
Spring 2020, Spring 2021, Website - 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
- 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. - 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
My very brief travel log
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