Cookies
We use essential cookies to run the site. Analytics cookies are optional and help us improve World Wide. Learn more.
Prof
Friedrich-Alexander-University
Showing your local timezone
Schedule
Tuesday, March 23, 2021
11:00 AM Europe/London
No session focus moments detected yet.
Recording provided by the organiser.
Domain
NeuroscienceHost
Imperial Centre for Neurotechnology
Duration
70 minutes
Understanding speech in noisy backgrounds requires selective attention to a particular speaker. Humans excel at this challenging task, while current speech recognition technology still struggles when background noise is loud. The neural mechanisms by which we process speech remain, however, poorly understood, not least due to the complexity of natural speech. Here we describe recent progress obtained through applying machine-learning to neuroimaging data of humans listening to speech in different types of background noise. In particular, we develop statistical models to relate characteristic features of speech such as pitch, amplitude fluctuations and linguistic surprisal to neural measurements. We find neural correlates of speech processing both at the subcortical level, related to the pitch, as well as at the cortical level, related to amplitude fluctuations and linguistic structures. We also show that some of these measures allow to diagnose disorders of consciousness. Our findings may be applied in smart hearing aids that automatically adjust speech processing to assist a user, as well as in the diagnosis of brain disorders.
Tobias Reichenbach
Prof
Friedrich-Alexander-University
Contact & Resources
neuro
Nervous system function relies on the polarized architecture of neurons, established by directional transport of pre- and postsynaptic cargoes. While delivery of postsynaptic components depends on the
neuro
A combinatorial neural code for long-term motor memory
neuro
The postdoctoral researcher will work on an interdisciplinary project at the intersection of deep learning and comparative politics. The candidate will work in the Human-Centered Machine Learning (HuM
We are looking for highly motivated researchers to join our group in interdisciplinary projects that focus on the development of computational models to understand how linguistic information is repres
The CSNG Lab at the Faculty of Mathematics and Physics at the Charles University is seeking a highly motivated Postdoctoral Researcher to join our team to work on a digital twin model of the visual sy
PROJECT SUMMARY / ABSTRACT Cluster headache (CH), which affects about 1 in 1,000 people, is a severe and debilitating primary headache disorder characterized by repeated attacks occurring in clusters