Robust dialogue act detection based on partial sentence tree, derivation rule, and spectral clustering algorithm

Chia Ping Chen, Chung Hsien Wu, Wei Bin Liang

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

A novel approach for robust dialogue act detection in a spoken dialogue system is proposed. Shallow representation named partial sentence trees are employed to represent automatic speech recognition outputs. Parsing results of partial sentences can be decomposed into derivation rules, which turn out to be salient features for dialogue act detection. Data-driven dialogue acts are learned via an unsupervised learning algorithm called spectral clustering, in a vector space whose axes correspond to derivation rules. The proposed method is evaluated in a Mandarin spoken dialogue system for tourist-information services. Combined with information obtained from the automatic speech recognition module and from a Markov model on dialogue act sequence, the proposed method achieves a detection accuracy of 85.1%, which is significantly better than the baseline performance of 62.3% using a naïve Bayes classifier. Furthermore, the average number of turns per dialogue session also decreases significantly with the improved detection accuracy.

Original languageEnglish
Article number13
JournalEurasip Journal on Audio, Speech, and Music Processing
Volume2012
Issue number1
DOIs
Publication statusPublished - 2012

All Science Journal Classification (ASJC) codes

  • Acoustics and Ultrasonics
  • Electrical and Electronic Engineering

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