Improving Fairness in Speaker Recognition and Speech Recognition
Abstract:
Group fairness, or avoidance of large performance disparities for different cohorts of users, is a major concern as AI technologies find adoption in ever more application scenarios. In this talk I will present some recent work on fairness for speech-based technologies, specifically, speaker recognition and speech recognition. For speaker recognition, I report on two algorithmic approaches to reduce performance variability across different groups. In the first method, group-adapted fusion, we combine sub-models that are specialized for subpopulations that have very different representation (and therefore performance) in the data. The second method, adversarial reweighting, forces the model to focus on those portions of the population that are harder to recognize, without requiring a priori labels for speaker groups. For automatic speech recognition, I present methods for detecting and mitigating accuracy disparities as a function of geographic or demographic variables, principally by oversampling or adaptation based on group membership. The talk concludes with an application of synthetic speech generation (TTS) for filling in data gaps for a group of speakers with atypical speech, namely, stutter.
Date and Time
Location
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Registration
- Date: 31 Mar 2023
- Time: 07:00 PM UTC to 08:00 PM UTC
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- Technology Square 75 5th St NW
- Atlanta, Georgia
- United States 30308
- Building: Centergy One Bldg.
- Room Number: CSIP Library
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In person event will be at:
Centergy One Bldg. CSIP Library, 5th Floor, Technology Square 75 5th St NW (Atlanta, GA 30308)
- Co-sponsored by Georgia Tech Center for Information and Signal Processing
- Starts 24 March 2023 08:00 PM UTC
- Ends 31 March 2023 08:00 PM UTC
- No Admission Charge
Speakers
Dr. Andreas Stolcke of Alexa Speech organization at Amazon
Improving Fairness in Speaker Recognition and Speech Recognition
Abstract:
Group fairness, or avoidance of large performance disparities for different cohorts of users, is a major concern as AI technologies find adoption in ever more application scenarios. In this talk I will present some recent work on fairness for speech-based technologies, specifically, speaker recognition and speech recognition. For speaker recognition, I report on two algorithmic approaches to reduce performance variability across different groups. In the first method, group-adapted fusion, we combine sub-models that are specialized for subpopulations that have very different representation (and therefore performance) in the data. The second method, adversarial reweighting, forces the model to focus on those portions of the population that are harder to recognize, without requiring a priori labels for speaker groups. For automatic speech recognition, I present methods for detecting and mitigating accuracy disparities as a function of geographic or demographic variables, principally by oversampling or adaptation based on group membership. The talk concludes with an application of synthetic speech generation (TTS) for filling in data gaps for a group of speakers with atypical speech, namely, stutter.
Related Publications:
Improving fairness in speaker verification via group-adapted fusion network
Adversarial reweighting for speaker verification fairness
Reducing geographic disparities in automatic speech recognition via elastic weight consolidation
Toward fairness in speech recognition: Discovery and mitigation of performance disparities
Stutter-TTS: Controlled synthesis and improved recognition of stuttered speech
Biography:
Andreas Stolcke is senior principal scientist in the Alexa Speech organization at Amazon. He obtained his PhD from UC Berkeley and then worked as a researcher at SRI International and Microsoft, before joining Amazon. His research interests include computational linguistics, language modeling, speech recognition, speaker recognition and diarization, and paralinguistics, with over 300 papers and patents in these areas. His open-source SRI Language Modeling Toolkit was widely used in academia (before becoming obsolete by virtue of deep neural network models). Andreas is a Fellow of the IEEE and the International Speech Communication Association, and giving this talk as an IEEE Distinguished Industry Speaker.