Multi-Resolution Cepstral Features for Phoneme Recognition Across Speech Sub-Bands

Abstract

Multi-resolution sub-band cepstral features strive to exploit discriminative cues in localised regions of the spectral domain by supplementing the full bandwith cepstral features with subband cepstral features derived from several levels of sub-band decomposition. Mult-iresolution feature vectors, formed by concatenation of the subband cepstral features into an extended feature vector, are shown to yield better performance than conventional MFCCs for phoneme recognition on the TIMIT database. Possible strategies for the recombination of partial recognition scores from independent multi-resoltuion sub-band models are explored. By exploiting the sub-band variations in signal to noise ratio for linearly weighted recombination of the log likelihood probabilities we obtained improved phoneme recognition performance in broadband noise compared to MFCC features. This is an advantage over a purely sub-band approach using non linear recombination which is robust only to narrow band noise.