Towards robust phoneme classification: Augmentation of PLP models with acoustic waveforms

The robustness of classification of phoneme segments using generative classifiers is investigated for the PLP and acoustic waveform speech representations in the presence of white Gaussian noise. We combine the strengths of both representations, specifically the excellent classification accuracy of PLP in quiet conditions with the additional robustness of acoustic waveform classifiers. This is achieved using a convex combination of their respective log-likelihoods to produce a combined decision function. The resulting combined classifier is uniformly as accurate as PLP alone and is significantly more robust to the presence of additive noise during testing. Issues of noise modelling and time-invariant classification of acoustic waveforms are also considered with initial solutions used to improve accuracy.