Leveraging kullbackLeibler divergence measures and information-rich cues for speech summarization

Shih Hsiang Lin; Yao Ming Yeh; Berlin Chen

doi:10.1109/TASL.2010.2066268

Leveraging kullbackLeibler divergence measures and information-rich cues for speech summarization

Shih Hsiang Lin, Yao Ming Yeh, Berlin Chen

Department of Computer Science and Information Engineering

Research output: Contribution to journal › Article › peer-review

19 Citations (Scopus)

Abstract

Imperfect speech recognition often leads to degraded performance when exploiting conventional text-based methods for speech summarization. To alleviate this problem, this paper investigates various ways to robustly represent the recognition hypotheses of spoken documents beyond the top scoring ones. Moreover, a summarization framework, building on the KullbackLeibler (KL) divergence measure and exploring both the relevance and topical information cues of spoken documents and sentences, is presented to work with such robust representations. Experiments on broadcast news speech summarization tasks appear to demonstrate the utility of the presented approaches.

Original language	English
Article number	5549862
Pages (from-to)	871-882
Number of pages	12
Journal	IEEE Transactions on Audio, Speech and Language Processing
Volume	19
Issue number	4
DOIs	https://doi.org/10.1109/TASL.2010.2066268
Publication status	Published - 2011 Apr 6

Keywords

KullbackLeibler (KL) -divergence
multiple recognition hypotheses
relevance information
speech summarization
topical information

ASJC Scopus subject areas

Acoustics and Ultrasonics
Electrical and Electronic Engineering

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