Computational characterization of supra-threshold hearing to understand speech-in-noise intelligibility deficits

Emmanuel Ponsot (Laboratoire des Systèmes Perceptifs, ENS)
November 20, 2019 — 11:00 — "Salle des séminaires du L2S"

Abstract

A largely unresolved problem in hearing sciences concerns the large heterogeneity observed among individuals with similar audiograms (hearing thresholds measured in quiet) in understanding speech in noisy environments. Recent studies suggest that supra-threshold auditory mechanisms (i.e. that operate above detection threshold) play a prominent role in these interindividual differences, but a precise view of where and how distortions arise along the auditory processing hierarchy is lacking. Addressing this problem requires novel approaches that not do simply consider hearing in terms of sensitivity, but in terms of fidelity of encoding. In this talk, I will present a novel methodological framework developed for this purpose, which combines signal-processing with psychoacoustical tests and computational modeling tools derived from system identification methods. I will present and discuss results from several experiments conducted in both normal-hearing and hearing-impaired individuals within this framework to characterize the processing of supra- threshold signals made of spectrotemporal modulations – broadband noises whose envelope is jointly modulated over time and frequency – which constitute the most crucial features underlying speech intelligibility. I will then explain how the detailed computational characterization returned from this joined experimental-modeling approach can be used to identify the different components underlying suprathreshold auditory encoding deficits. Overall, this project describes an innovative approach that capitalizes on system- engineering methods to shed an unprecedented light on supra-threshold hearing and its disorders. By integrating the knowledge of how the auditory system operates above the threshold in noisy conditions, this project will generate new avenues for the development of novel audiological procedures and signal-processing strategies for hearing aids.

Biography

Emmanuel Ponsot was initially trained in Engineering at Ecole Centrale (Lyon), and received a Master degree in Acoustics in 2012. He then turned to Psychoacoustics and Cognitive Sciences and obtained a Ph.D. from Sorbonne Université in 2015 on loudness processing and coding in humans. From 2015 to 2017, he did a first postdoc at IRCAM (Paris), during which he developed new tools to explore the computational bases of Social Cognition in speech prosody. He is currently a post-doctoral researcher at the Laboratoire des Systèmes Perceptifs (ENS, Paris), where he combines experimental and modeling approaches to characterize the auditory mechanisms used to process complex supra-threshold signals in noise, in both normal-hearing individuals and individuals with hearing loss.