Coupling Particle Filters with Auto for Speech Feature E

This paper addresses robust speech feature extraction in combina-tion with statistical speech feature enhancement and couples the particle filter to the speech recognition hypotheses. To extract noise robust features the Fourier transformation is replaced by the warped and scaled minimum variance distortion-less response spectral envelope. To enhance the features, particle filtering has been used. Further, we show that the robust extraction and statistical enhancement can be combined to good effect. One of the critical aspects in particle filter design is the par-ticle weight calculation which is traditionally based on a general, time independent speech model approximated by a Gaussian mix-ture distribution. We replace this general, time independent speech model by time- and phoneme-specific models. The knowledge of the phonemes to be used is obtained by the hypothesis of a speech recognition system, therefore establishing a coupling between the particle filter and the speech recognition system which have been treated as independent components in the past. Index Terms: particle filters, automatic speech recognition, speech feature enhancement, phoneme-specifi