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International: An Analysis of Speaker Diarization Systems Errors

International: An Analysis of Speaker Diarization Systems Errors
国际:说话人二值化系统误差分析
批准号:
1135365
负责人:
Nelson Morgan
金额:
$1.5万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2011
资助国家:
美国
项目状态:
已结题
起止时间:
2011-08-01 至 2012-07-31

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中文摘要
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英文摘要
This project will support the three-month visit of a US PhD student to the Idiap Research Institute in Switzerland, a leading international laboratory that has developed a state-of-the-art diarization system. Speaker diarization is the task of determining ?who spoke when? without a priori knowledge of the number of speakers or speaker identities. The focus of the current effort is to perform error analysis in audio-only speaker diarization for the meeting domain. There are two main areas of interest. The first is to build a framework to analyze speaker diarization performance on specific types of segments (e.g., speaker changes, interruption, overlapped speech, short utterances, long utterances, etc.). By analyzing where speaker diarization systems perform poorly, speaker diarization researchers can focus on improving performance during those problematic types of segments. The second area is to compare speaker diarization performance across systems. The project has substantial broader impacts. Speaker diarization is a useful step in meeting analysis. Considering the time people spend in meetings, improved speaker diarization could be useful for a broad portion of the population. While the goal is to characterize current speaker diarization errors, the knowledge gained from this work will be useful for improving future speaker diarization systems. In particular, by comparing where errors occur across multiple systems, the speaker diarization community can gain insight into the strengths and weaknesses of the various systems which could lead to a more novel way of combining systems to improve speaker diarization performance. In addition, the project will support the development of an international network of collaborators for a US graduate student.
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会议论文
RI: Small: Collaborative Research: Towards Modeling Source Separation from Measured Cortical Responses
EAGER: Collaborative Research: Towards Modeling Human Speech Confusions in Noise
CI-P: Towards a Consensus Representation for Understanding Structure of Multiparty Conversations
OIA/MRI: Acquisition of a Computational Server for Large Vocabulary Connectionist Speech Recognition
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