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ROBUSTNESS OF PHONEME CLASSIFICATION USING GENERATIVE CLASSIFIERS: COMPARISON OF THE ACOUSTIC WAVEFORM AND PLP REPRESENTATIONS

The robustness of classification of isolated phoneme segments using generative classifiers is investigated for the acoustic waveform and PLP speech representations. Probabilistic PCA is used to fit a density to each phoneme class followed by maximum likelihood classification. The results show that although PLP performs best in quiet conditions, as the SNR decreases below 0dB acoustic waveforms have a lower classification error. This is the case even though the waveform classifier is trained explicitly only on quiet data and is then modified by a simple transformation to account for the noise, whereas for PLP separate classifiers are trained for each noise condition. Even at −18dB SNR, multiclass performance of classification from waveforms is still significantly better than chance level. In addition the effect of time-alignment is tested and initial solution shown.