An HMM-Based Subband Processing Approach to Speaker Identification

Authors:
Jonathan E. Higgins;Robert I. Damper
Affiliations:
-;-
Venue:
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Year:
2001

Citing 6
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Abstract

This paper contributes to the growing literature confirming the effectiveness of subband processing for speaker recognition. Specifically, we investigate speaker identification from noisy test speech modelled using linear prediction and hidden Markov models (HMMs). After filtering the wideband signal into subbands, the output time trajectory of each is represented by 12 pseudo-cepstral coefficients which are used to train and test individual HMMs. During recognition, the HMM outputs are combined to produce an overall score for each test utterance. We find that, for particular numbers of filters, subband processing outperforms traditional wideband techniques.