The Gamma MLP for Speech Phoneme Recognition
Steve Lawrence, Ah Chung Tsoi, Andrew D. Back · 1995
We define a Gamma multi-layer perceptron (MLP) as an MLP with the usual synaptic weights replaced by gamma filters (as proposed by de Vries and Principe (de Vries & Principe 1992)) and associated gain terms throughout all layers. We derive gradient descent update equations and apply the model to the recognition of speech phonemes. We find that both the inclusion of gamma filters in all layers, and the inclusion of synaptic gains, improves the performance of the Gamma MLP. We compare the Gamma MLP with TDNN, Back-Tsoi FIR MLP, and Back-Tsoi IIR MLP architectures, and a local approximation scheme. We find that the Gamma MLP results in a substantial reduction in error rates. 1 INTRODUCTION 1.1 THE GAMMA FILTER Infinite Impulse Response (IIR) filters have a significant advantage over Finite Impulse Response (FIR) filters in signal processing: the length of the impulse response is uncoupled from the number of filter parameters. The length of the impulse response is related to the memory d...