New cepstrum frequency scale for neural network speaker verification

Abstract

The influence of cepstrum parameters on text-dependent speaker verification and speech recognition is investigated. Experiments are performed to establish the relevance of various resonant frequencies and frequency bands in terms of their speech and speaker recognition ability. A Romanian database of eighteen isolated words has been used. The study of the filter bank analysis suggests a new frequency scale instead of the currently used mel-scale to extract from the speech signal cepstrum coefficients. The proposed scale results in better performance in speaker verification. The processes of speech recognition and speaker verification are carried out by using a neural network system comprising a self-organizing feature map (SOFM) and a multilayer perceptron (MLP).