Perceptive, non-linear Speech Processing and Spiking Neural Networks

TL;DR

This paper explores biologically inspired non-linear speech processing techniques, combining perceptive analysis with neural networks to improve source separation and recognition in noisy environments.

Contribution

It introduces a novel approach integrating perceptive speech analysis with neural networks inspired by auditory scene analysis for improved speech processing.

Findings

Demonstrated potential of non-linear processing for source separation

Proposed biologically plausible neural network models for speech analysis

Discussed applications in speech recognition

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.