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Robustness of phoneme classification using generative classifiers: comparison of the acoustic waveform and plp representations.
| Content Provider | CiteSeerX |
|---|---|
| Author | Ager, Matthew Cvetković, Zoran Sollich, Peter Yousafzai, Jibran |
| Abstract | 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. Index Terms — Speech Recognition, Robustness, Generative Classification 1. |
| File Format | |
| Access Restriction | Open |
| Content Type | Text |
