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Inference, Prediction, and Talker Variability

Date and Time: 
Thursday, May 9, 2019. 03:00 PM - 04:30 PM
Meeting Location: 
Building 460, Room 126
Cognition and Language Workshop 2017
Meeting Description: 

On the one hand, talker variability is one of the fundamental challenges for speech recognition: each talker has their own mapping from linguistic units to sounds, which means that an effective listener must use a different recognition function for each talker. On the other hand, talker variability means that speech is a source of rich information about who the talker is. This dual nature of talker variability means that speech and talker recognition are inextricably linked: knowing something about who is talking makes it easier to understand what they are saying, and knowing something about how someone talks unlocks the rich social meaning of speech. Dr. Keinschmidt argues that the concept of a talker's generative model, or the probabilistic distributions of sounds associated with each phonetic/linguistic category, is a useful general purpose conceptual tool for understanding the link between talker variability, speech recognition, and social identity. With such phonetic cue distributions, we can use information theoretic tools to quantify both the extent and structure of talker variability across different phonetic systems, and establish in-principle consequences of talker variability for both speech recognition and socio-indexical inferences from speech. These mathematical tools are deeply connected with the ideas of inference and prediction, and allow us to pose—and answer—questions about what kind of predictions listeners can and should make at multiple levels, based on the structure of variation within and across talkers.

Dave Kleinschmidt is professor of psychology at Rutgers University.