WORD-CLASS STOCHASTIC MODEL IN A SPOKEN LANGUAGE DIALOGUE SYSTEM

Spoken Language Understanding (SLU) is a key component of spoken dialogue systems. One popular SLU method is to use the continuous speech recognizer where the Part-Of-Speech (POS) tagging is employed to determine the underlying word-class sequences. We present here a Word-Class Stochastic Model (WCSM) to describe the temporal word/word-class sequences, which is fit into the standard paradigm of the Hidden Markov Models (HMMs). The model training is done on the basis of a general-purposed, large-vocabulary-sized, labeled corpus, which makes the model comparatively easy to construct. We apply the model to a prototype dialogue system named EasyNav, and the use of domain-specific knowledge, i.e., semantic-meaningfu