Perceptive, Non-linear Speech Processing and Spiking Neural Networks

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 networks processors. We illustrate the potential of such non-linear processing of speech on two applications. The first is a source separation system inspired by Auditory Scene Analysis paradigm and the second is a crude spoken digit recogniser. We present preliminary results and discuss them. keywords: Auditory modelling, Source separation, Amplitude Modulation, Auditory Scene Analysis, Spiking Neurones, Temporal Correlation, Multiplicative Synapses, Cochlear Nucleus, Corrupted Speech Processing, Rank Order Coding, Speech recognition.