Spoken Dialogue System Using Corpus-Based Hidden Markov Model

Chung Hsien Wu, Gwo Lang Yan, Chien Liang Lin

研究成果: Paper同行評審

11 引文 斯高帕斯(Scopus)


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.

出版狀態Published - 1998
事件5th International Conference on Spoken Language Processing, ICSLP 1998 - Sydney, Australia
持續時間: 1998 11月 301998 12月 4


Conference5th International Conference on Spoken Language Processing, ICSLP 1998

All Science Journal Classification (ASJC) codes

  • 語言與語言學
  • 語言和語言學


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