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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
翻译
该项目将支持一名美国博士生对瑞士Idiap研究所进行为期三个月的访问,该研究所是一家领先的国际实验室,开发了最先进的二值化系统。确定演讲者身份的任务是什么?谁在什么时候发言?而没有说话者数量或说话者身份的先验知识。当前工作的重点是对会议域中的纯音频说话人二值化进行误差分析。人们感兴趣的主要有两个领域。第一个是建立一个框架来分析特定类型的片段(例如,说话人改变、中断、重叠语音、短话语、长话语等)上的说话人二元化性能。通过分析说话人二元化系统在哪里表现不佳,说话人二元化研究人员可以专注于提高这些有问题的分段类型的性能。第二个领域是比较不同系统的扬声器二元化性能。该项目具有重大而广泛的影响。说话人对分是会议分析中一个有用的步骤。考虑到人们花在会议上的时间,改进发言者二元化对大部分人来说可能是有用的。虽然目标是表征当前的说话人二元化错误,但从这项工作中获得的知识将有助于改进未来的说话人二元化系统。特别是,通过比较多个系统中发生错误的位置,扬声器对分社区可以深入了解各种系统的优势和劣势,这可能导致一种更新颖的组合系统以提高扬声器对分性能的方法。此外,该项目还将支持为一名美国研究生建立一个国际合作者网络。
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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