PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Towards robust phoneme classification: Augmentation of PLP models with acoustic waveforms
Matthew Ager, Zoran Cvetkovic, Peter Sollich and Bin Yu
In: EUSIPCO 2008, Lausanne(2008).


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 show a method to 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 loglikelihoods. Issues of noise modelling and time-invariance of acoustic waveforms are also addressed with initial solutions shown. The resulting combined classifier has greater accuracy than PLP alone and is significantly more robust to the presence of additive noise during testing.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:4050
Deposited By:Peter Sollich
Deposited On:25 February 2008