文章基本信息

标题：Integrating Prosodic and Lexical Cues for Automatic Topic Segmentation
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作者：Gökhan Tür ; Dilek Hakkani-Tür ; Andreas Stolcke 等
期刊名称：Computational Linguistics
印刷版ISSN：0891-2017
电子版ISSN：1530-9312
出版年度：2001
卷号：27
期号：1
页码：31-57
DOI：10.1162/089120101300346796
语种：English
出版社：MIT Press
摘要：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.