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.Comment: 27 pages, 8 figure

Andreas Stolcke

Dilek Hakkani-Tür

Elizabeth Shriberg

Grosz B.

Gökhan Tür

Hearst Marti A

Passonneau Rebecca J

Computational Linguistics

English

arXiv

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

Tür G.

Stolcke, A.

Hakkani-Tür, D.

Shriberg, E.

Bilkent University Institutional Repository

Integrating prosodic and lexical cues for automatic topic segmentation

SRI International SRI International 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. 1

Dilek Hakkani-tür

CiteSeerX

 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. </jats:p

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<b>Integrating Prosodic and Lexical Cues for Automatic Topic Segmentation</b>

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.342.3102

Integrating Prosodic and Lexical Cues for Automatic Topic Segmentation

Integrating Prosodic and Lexical Cues for Automatic Topic Segmentation

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