Speech Recognition Front End Without Information Loss

Phoneme classification is investigated for linear feature domains with the aim of improving robustness to additive noise. In linear feature domains noise adaptation is exact, potentially leading to more accurate classification than representations involving non-linear processing and dimensionality reduction. A generative framework is developed for isolated phoneme classification using linear features. Initial results are shown for representations consisting of concatenated frames from the centre of the phoneme, each containing f frames. As phonemes have variable duration, no single f is optimal for all phonemes, therefore an average is taken over models with a range of values of f . Results are further improved by including information from the entire phoneme and transitions. In the presence of additive noise, classification in this framework performs better than an analogous PLP classifier, adapted to noise using cepstral mean and variance normalisation, below 18dB SNR. Finally we propose classification using a combination of acoustic waveform and PLP log-likelihoods. The combined classifier performs uniformly better than either of the individual classifiers across all noise levels.