Interruption point detection of spontaneous speech using prior knowledge and multiple features

Wei Bin Liang, Jui Feng Yeh, Chung Hsien Wu, Chi Chiuan Liou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper presents an approach to interruption point (IP) detection of spontaneous speech based on conditional random fields using prior knowledge and multiple features. The features adopted in this study consist of subsyllable boundaries and prosodic features. Conditional random fields (CRFs) and variable-length contextual features are employed for IP modeling. In order to apply the features with continuous values to the CRF models, the K-means clustering algorithm is adopted for the quantization of the prosodic features. In the experimental results, Mandarin Conversional Dialogue Corpus (MCDC) was used to evaluate the proposed method. The IP detection error rate achieved almost 20% reduction in Rt04 measure. The experimental results show that the proposed model can effectively detect the interruption point in spontaneous speech.

Original languageEnglish
Title of host publication2008 IEEE International Conference on Multimedia and Expo, ICME 2008 - Proceedings
Pages1457-1460
Number of pages4
DOIs
Publication statusPublished - 2008
Event2008 IEEE International Conference on Multimedia and Expo, ICME 2008 - Hannover, Germany
Duration: 2008 Jun 232008 Jun 26

Publication series

Name2008 IEEE International Conference on Multimedia and Expo, ICME 2008 - Proceedings

Other

Other2008 IEEE International Conference on Multimedia and Expo, ICME 2008
Country/TerritoryGermany
CityHannover
Period08-06-2308-06-26

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering

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