Integrating Prosodic and Lexical Cues for Automatic Topic Segmentation

Gökhan Tür, Dilek Hakkani-Tür, Andreas Stolcke, Elizabeth Shriberg
<span title="">2001</span> <i title="MIT Press - Journals"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/e4yflo6sufcufjwloald7xudm4" style="color: black;">Computational Linguistics</a> </i> &nbsp;
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
more &raquo; ... w 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1162/089120101300346796">doi:10.1162/089120101300346796</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cnz4bvenjbgq3jwzts2zwe5224">fatcat:cnz4bvenjbgq3jwzts2zwe5224</a> </span>
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