Perceptive, non-linear Speech Processing and Spiking Neural Networks
Abstract
Source separation and speech recognition are very difficult in the context of noisy and corrupted speech. Most conventional techniques need huge databases to estimate speech (or noise) density probabilities to perform separation or recognition. We discuss the potential of perceptive speech analysis and processing in combination with biologically plausible neural network processors. We illustrate the potential of such non-linear processing of speech on a source separation system inspired by an Auditory Scene Analysis paradigm. We also discuss a potential application in speech recognition.
Cite
@article{arxiv.2204.00094,
title = {Perceptive, non-linear Speech Processing and Spiking Neural Networks},
author = {Jean Rouat and Ramin Pichevar and Stéphane Loiselle},
journal= {arXiv preprint arXiv:2204.00094},
year = {2022}
}
Comments
preprint of the 2005 published paper: Perceptive, Non-linear Speech Processing and Spiking Neural Networks. In: Chollet, G., Esposito, A., Faundez-Zanuy, M., Marinaro, M. (eds) Nonlinear Speech Modeling and Applications. NN 2004. Lecture Notes in Computer Science, vol 3445. Springer, Berlin, Heidelberg