Name
Abstract | ||
|---|---|---|
Understanding the neural dynamics underlying the fast discrimination of music and speech in noise is a very challenging task for neurocomputational and speech recognition models. In this paper, we present a model of interacting neural ensembles which includes a top-down modulation of the peripheral system dynamics, based on bottom-up perceptual predictions. This bi-directional processing could enable the detection of sudden changes in the input sounds in noise; advancing in the understanding of how listeners can improve their perception by focusing their attention. Our preliminary work opens the possibility of developing a pioneering class of neurophysiological-based speech processors for cochlear implants and speech recognition devices under degraded conditions. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-34481-7_33 | ICONIP |
Keywords | Field | DocType |
neural dynamic,speech recognition model,challenging task,speech recognition device,bi-directional processing,neural ensemble,neurodynamical top-down processing,degraded condition,bottom-up perceptual prediction,auditory attention,neurophysiological-based speech processor,cochlear implant,speech recognition | Neurophysiology,Computer science,Top-down and bottom-up design,Speech recognition,System dynamics,Neurocomputational speech processing,Perception | Conference |
Volume | ISSN | Citations |
7664 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Emili Balaguer-Ballester | 1 | 59 | 10.97 |
| Abdelhamid Bouchachia | 2 | 1001 | 54.20 |
| Beibei Jiang | 3 | 0 | 0.34 |
| Susan L. Denham | 4 | 99 | 12.32 |