TAILIEUCHUNG - Báo cáo khoa học: "Summarizing multiple spoken documents: finding evidence from untranscribed audio"

This paper presents a model for summarizing multiple untranscribed spoken documents. Without assuming the availability of transcripts, the model modifies a recently proposed unsupervised algorithm to detect re-occurring acoustic patterns in speech and uses them to estimate similarities between utterances, which are in turn used to identify salient utterances and remove redundancies. This model is of interest due to its independence from spoken language transcription, an error-prone and resource-intensive process, its ability to integrate multiple sources of information on the same topic, and its novel use of acoustic patterns that extends previous work on low-level prosodic feature detection. . | Summarizing multiple spoken documents finding evidence from untranscribed audio Xiaodan Zhu Gerald Penn and Frank Rudzicz University of Toronto 10 King s College Rd Toronto M5S 3G4 ON Canada xzhu gpenn frank @ Abstract This paper presents a model for summarizing multiple untranscribed spoken documents. Without assuming the availability of transcripts the model modifies a recently proposed unsupervised algorithm to detect re-occurring acoustic patterns in speech and uses them to estimate similarities between utterances which are in turn used to identify salient utterances and remove redundancies. This model is of interest due to its independence from spoken language transcription an error-prone and resource-intensive process its ability to integrate multiple sources of information on the same topic and its novel use of acoustic patterns that extends previous work on low-level prosodic feature detection. We compare the performance of this model with that achieved using manual and automatic transcripts and find that this new approach is roughly equivalent to having access to ASR transcripts with word error rates in the 33-37 range without actually having to do the ASR plus it better handles utterances with out-ofvocabulary words. 1 Introduction Summarizing spoken documents has been extensively studied over the past several years Penn and Zhu 2008 Maskey and Hirschberg 2005 Murray et al. 2005 Christensen et al. 2004 Zechner 2001 . Conventionally called speech summarization although speech connotes more than spoken documents themselves it is motivated by the demand for better ways to navigate spoken content and the natural difficulty in doing so speech is inherently more linear or sequential than text in its traditional delivery. Previous research on speech summarization has addressed several important problems in this field see Section . All of this work however has focused on single-document summarization and the integration of fairly simplistic .

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• Summarizing multiple spoken documents
• finding evidence
• untranscribed audio
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