Pronunciation modeling for spontaneous speech recognition using latent pronunciation analysis (LPA) and prior knowledge

In this paper, we propose a new framework for pronunciation modeling, in which the search algorithm tries to focus primarily on the clearly-pronounced portion of speech, while deemphasizing the observations of the slurred portion. This is based on the prior analysis that the pronunciation variation has to do with the predictability and the importance of the words in the spoken utterances, which may be estimated to some extent. We define a set of pronunciation-related features and develop a Latent Pronunciation Analysis (LPA) to estimate the "latent pronunciation states" in the speech. The LPA probabilities, pronunciation-related features and another set of prior knowledge obtained from two distance measures between phonemes are integrated in a SVM classifier to produce a "pronunciation variation indicator" for each frame, based on which the Viterbi decoding was performed. Very encouraging initial results on Mandarin spontaneous speech were obtained in preliminary experiments.