Integrating prosodic and lexical cues for automatic topic segmentation

  • Authors:

  • Gökhan Tür;Andreas Stolcke;Dilek Hakkani-Tür;Elizabeth Shriberg

  • Affiliations:

  • Bilkent University;SRI International;Bilkent University;SRI International

  • Venue:
  • Computational Linguistics
  • Year:
  • 2001

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Abstract

We present a probabilistic model that uses both prosodic and lexical cues for the automatic segmentation of speech into topically coherent units. We propose two methods for combining lexical and prosodic information using hidden Markov models and decision trees. Lexical information is obtained from a speech recognizer, and prosodic features are extracted automatically from speech waveforms. We evaluate our approach on the Broadcast News corpus, using the DARPA-TDT evaluation metrics. Results show that the prosodic model alone is competitive with word-based segmentation methods. Furthermore, we achieve a significant reduction in error by combining the prosodic and word-based knowledge sources.