Robust phoneme discrimination using acoustic waveforms

We present a study of separability of acoustic waveforms of speech at phoneme level. The analyzed data consist of 64ms segments of acoustic waveforms of individual phonemes from TIMIT data base, sampled at 16kHz. For each phoneme, by means of principal component analysis, we identify subspaces which contain a given proportion of the total energy of the available waveforms in time-domain, and also in spectral-magnitude domain. In order to assess separation between phonemes in the two domains, we perform pairwise classification of phonemes on clean data and on data immersed in white additive Gaussian noise up to 0dB signal to noise ratio. While the classification based on spectral magnitudes exhibits high sensitivity to additive noise, the time-domain classification proves to be very robust.