Given the ubiquity of Machine Learning (ML) systems and their relevance in daily lives, it is important to ensure private and safe handling of data alongside equity in human experience. These considerations have gained considerable interest in recent times under the realm of Trustworthy ML. Speech processing in particular presents a unique set of challenges, given the rich information carried in linguistic and paralinguistic content including speaker trait, interaction and state characteristics. This special session on Trustworthy Speech Processing (TSP) was created to bring together new and experienced researchers working on trustworthy ML and speech processing. We invite novel and relevant submissions from both academic and industrial research groups showcasing theoretical and empirical advancements in TSP.
Topics of interest cover a variety of papers centered on speech processing, including (but not limited to):
- Differential privacy
- Federated learning
- Ethics in speech processing
- Model interpretability
- Quantifying & mitigating bias in speech processing
- New datasets, frameworks and benchmarks for TSP
- Discovery and defense against emerging privacy attacks
- Trustworthy ML in applications of speech processing like ASR
- Anil Ramakrishna, Amazon Inc.
- Shrikanth Narayanan, University of Southern California
- Rahul Gupta, Amazon Inc.
- Isabel Trancoso, University of Lisbon
- Rita Singh, Carnegie Mellon University
Call for papers
Submissions for TSP will follow the same schedule and procedure as the main conference. To submit your papers, click here (select option #14.13 as the submission topic for your paper).
- Paper submission deadline: March 21, 2022, 23:59, Anywhere on Earth.
- Paper update deadline: March 28, 2022, 23:59, Anywhere on Earth.
- Author notification: June 13, 2022.
- Interspeech conference dates: September 18 to 22, 2022.
If you have any questions, please contact us at email@example.com.