Multi-stream spectro-temporal and cepstral features based on data-driven hierarchical phoneme clusters

Abstract

We propose a method to enhance multi-stream Gabor and MFCC features using data-driven hierarchical phoneme clusters to yield more discriminating posteriors. We take into account different hierarchy structures, and in addition perform mean and variance normalization. A relative improvement of 11.5% over the conven tional MFCC Tandem system was achieved in experiments con ducted on Mandarin broadcast news. We analyze the complemen tarity between Gabor and MFCC features for different types of phonemes, and investigate the benefits that come from using hie rarchical phoneme clusters.