Spoken dialogue system using corpus-based hidden Markov model

In a spoken dialogue system, the intention is the most important component for speech understanding. In this paper, we propose a corpus-based hidden Markov model (HMM) to model the intention of a sentence. Each intention is represented by a sequence of word segment categories determined by a task-specific lexicon and a corpus. In the training procedure, five intention HMM’s are defined, each representing one intention in our approach. In the intention identification process, the phrase sequence is fed to each intention HMM. Given a speech utterance, the Viterbi algorithm is used to find the most likely intention sequences. The intention HMM considers not only the phrase frequency but also the syntactic and semantic structure in a phrase sequence. In order to evaluate the proposed method, a spoken dialogue model for air travel information service is investigated. The experiments were carried out using a test database from 25 speakers (15 male and 10 female). There are 120 dialogues, which contain 725 sentences in the test database. The experimental results show that the correct response rate can achieve about 80.3% using intention HMM.