Feature extraction using a matrix coefficient filter for speech recognition

Abstract

This paper provides a new interpretation of the so-called “delta-cepstrum” and extends the formulation of the conventional delta-cepstrum towards an optimal design of the filter, which extracts important spectral dynamics from a cepstrum sequence. The algorithm to obtain new feature parameters is unified to a formulation using a matrix coefficient filter and is tested through Japanese speech recognition experiments. The average recognition error rate in a Japanese 24 phoneme recognition experiment for four speakers was reduced from 12.2% to 10.3%.