Pre-Grant Publication Number: 20100268534
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Prior Art Detail
Summary / Description
| Summary / Description | The authors discuss solutions for retrieving information from conversational speech corpora, e.g., call-center data. |
Basic Information
| Type of Prior Art | Print Publication |
| Publication Title * | Spoken document retrieval from call-center conversations |
| Author | Mamou, et al. |
| ISBN | |
| Page Range | 1-8 |
| Medium | Journal article |
| Publication Date * | August 6, 2006 |
| URL | http://portal.acm.org/citation.... |
Notes / To Do
| Notes | |
Excerpt
Excerpt Abstract: We are interested in retrieving information from conversational speech corpora, such as call-center data. This data comprises spontaneous speech conversations with low recording quality, which makes automatic speech recognition (ASR) a highly difficult task. For typical call-center data, even state-of-the-art large vocabulary continuous speech recognition systems produce a transcript with word error rate of 30% or higher. In addition to the output transcript, advanced systems provide word confusion networks (WCNs), a compact representation of word lattices associating each word hypothesis with its posterior probability. Our work exploits the information provided by WCNs in order to improve retrieval performance. In this paper, we show that the mean average precision (MAP) is improved using WCNs compared to the raw word transcripts. Finally, we analyze the effect of increasing ASR word error rate on search effectiveness. We show that MAP is still reasonable even under extremely high error rate. |
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Claims
1
Relevance
The authors in the article describe a solution for retrieving information from conversational speech corpora for output into as a transcript.
The authors in the article describe a solution for retrieving information from conversational speech corpora for output into as a transcript.
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