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
In: EUSIPCO 2009, 24-28 Aug 2009, Glasgow, Scotland.

Abstract

The robustness of classification of isolated phoneme segments us- 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 for 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:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Speech
ID Code:6099
Deposited By:Matthew Ager
Deposited On:24 February 2010