PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Robust Phoneme Classification: Exploiting The Adaptability of Acoustic Waveform Models
Matthew Ager, Zoran Cvetkovic and Peter Sollich
Robust Phoneme Classification: Exploiting The Adaptability of Acoustic Waveform Models 2009.

Abstract

The robustness of classification of isolated phoneme segments using generative classifiers is investigated for the acoustic waveform, MFCC and PLP speech representations. Gaussian mixture models with diagonal covariance matrices are used followed by maximum likelihood classification. The performance of noise adapted acoustic waveform models is compared with PLP and MFCC models that were adapted using noisy training set feature standardisation. In the presence of additive noise acoustic waveforms have significantly lower classification error. Even in the unrealistic case where PLP and MFCC classifiers are trained and tested in exactly matched noise conditions acoustic waveform classifiers still outperform them. In both cases the acoustic waveform classifiers are trained explicitly only on quiet data and then modified by a simple transformation to account for the noise.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Speech
ID Code:5169
Deposited By:Peter Sollich
Deposited On:24 March 2009