Multi-resolution cepstral features for phoneme recognition across speech sub-bands

Multi-resolution sub-band cepstral features strive to exploit discriminative cues in localised regions of the spectral domain by supplementing the full bandwidth cepstral features with sub-band cepstral features derived from several levels of sub-band decomposition. Multi-resolution feature vectors, formed by concatenation of the sub-band 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-resolution 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