Dan Roth (University of Pennsylvania)

Sakriani Sakti (Japan Advanced Institute of Science and Technology)

Semi-supervised Learning for Low-resource Multilingual and Multimodal Speech Processing with Machine Speech Chain (May 5, 2022, 15:00 CET)   

Coming soon. 

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Odette Scharenborg (Delft University of Technology)

Speech Representations and Processing in Deep Neural Networks (January 13, 2022, 15:00 CET)   

Summary:
Abstract Speech recognition is the mapping of a continuous, highly variable speech signal onto discrete, abstract representations. The question of how speech is represented and processed in the human brain and in automatic speech recognition (ASR) systems, although crucial in both the field of human speech processing and the field of automatic speech processing, has historically been investigated in the two fields separately. This webinar will discuss how comparisons between humans and deep neural network (DNN)-based ASRs, and cross-fertilization of the two research fields, can provide valuable insights into the way humans process speech and improve ASR technology. Specifically, it will present results of several experiments carried out on both human listeners and DNN-based ASR systems on the representation of speech in human listeners and DNNs and on lexically-guided perceptual learning, i.e., the ability to adapt a sound category on the basis of new incoming information resulting in improved processing of subsequent information. It will explain how listeners adapt to the speech of new speakers, and will present the results of a lexically-guided perceptual study carried out on a DNN-based ASR system, similar to the human experiments. In order to investigate the speech representations and adaptation processes in the DNN-based ASR systems, activations in the hidden layers of the DNN were visualized. These visualizations revealed that DNNs use speech representations that are similar to those used by human listeners, without being explicitly taught to do so, and showed an adaptation of the phoneme categories similar to what is assumed happens in the human brain.
Bio: Odette Scharenborg is an Associate Professor and Delft Technology Fellow at Delft University of Technology working on automatic speech processing. She has an interdisciplinary background in automatic speech recognition and psycholinguistics, and uses knowledge from how humans process speech in order to develop inclusive automatic speech recognition systems that are able to recognise speech from everyone, irrespective of how they speak or the language they speak. Since 2017, she is on the Board of the International Speech Communication Association, and currently serves as Vice-President. Since 2018, she is on the IEEE Speech and Language Processing Technical Committee, and she is a Senior Associate Editor of IEEE Signal Processing Letters.

Sam Bowman (New York University)

When Combating Hype, Proceed with Caution (December 2, 2021, 15:00 CET)   

Summary: Researchers in NLP increasingly frame and discuss research results in ways that serve to deemphasize the field's successes, at least in part in an effort to combat the field's widespread hype. Though well-meaning, this often yields misleading or even false claims about the limits of our best technology. This is a problem, and it may be more serious than it looks: It harms our credibility in ways that can make it harder to mitigate present-day harms, from NLP deployments, like those involving discriminatory systems for content moderation or resume screening. It also limits our ability to prepare for the potentially enormous impacts of more distant future advances. This talk urges researchers to be careful about these claims and suggests some research directions and communication strategies that will make it easier to avoid or rebut them.
Bio: Sam Bowman has been on the faculty at NYU since 2016, when he completed PhD with Chris Manning and Chris Potts at Stanford. At NYU, he is a member of the Center for Data Science, the Department of Linguistics, and Courant Institute's Department of Computer Science. His research focuses on data, evaluation techniques, and modeling techniques for sentence and paragraph understanding in natural language processing, and on applications of machine learning to scientific questions in linguistic syntax and semantics. He is the senior organizer behind the GLUE and SuperGLUE benchmark competitions and he has received a 2015 EMNLP Best Resource Paper Award, a 2019 *SEM Best Paper Award, a 2017 Google Faculty Research Award, and a 2021 NSF CAREER award.

Hinrich Schuetze (University of Munich)