Speaker Adaptation of Language Models for Automatic Dialog Act Segmentation of Meetings

Abstract

Abstract Dialog act (DA) segmentation in meeting speech is important for meeting understanding. In this paper, we explore speaker adaptation of hidden event language models (LMs) for DA segmentation using the ICSI Meeting Corpus. Speaker adaptation is performed using a linear combination of the generic speaker independent LM and an LM trained on only the data from individual speakers. We test the method on 20 frequent speakers, on both reference word transcripts and the output of automatic speech recognition. Results indicate improvements for 17 speakers on reference transcripts, and for 15 speakers on automatic transcripts. Overall, the speaker-adapted LM yields statistically significant improvement over the baseline LM for both test conditions.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2007
Accession Number
ADA469307

Entities

People

  • Elizabeth Shriberg
  • Jachym Kolar
  • Yang Liu

Organizations

  • University of West Bohemia

Tags

DTIC Thesaurus Topics

  • Applied Computer Science
  • Automata Theory
  • Automated Speech Recognition
  • Automated Text Summarization
  • Automatic
  • Computational Science
  • Computer Languages
  • Computer Science
  • Computer Vision
  • Hidden Markov Models
  • Language
  • Markov Models
  • Natural Language Processing
  • Probability
  • Recognition
  • Supervised Machine Learning
  • Test Sets

Fields of Study

  • Engineering
  • Linguistics

Readers

  • Speech Processing/Speech Recognition.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Translation