Identification and modeling of word fragments in spontaneous speech
This paper presents a novel approach to handling disfluencies, word fragments and self-interruption points in Cantonese conversational speech. We train a classifier that exploits lexical and acoustic information to automatically identify disfluencies during training of a speech recognition system on conversational speech, and then use this classifier to augment reference annotations used for acoustic model training. We experiment with approaches to modeling disfluencies in the pronunciation dictionary, and their effect on the polyphonic decision tree clustering. We achieve automatic detection of disfluencies with 88% accuracy, which leads to a reduction in character error rate of 1.9% absolute. While the high baseline error rates are due to the task we are currently working on, we demonstrate that this approach works well on the Switchboard corpus, for which the conversational nature of speech is also a major problem.