Robust Speaker segmentation and clustering for Meetings
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چکیده
منابع مشابه
Nanyang Technological University Speaker Diarization in Meetings Domain
The purpose of this study is to develop robust techniques for speaker segmentation and clustering with focus on meetings domain. The techniques examined can however be applied to any other domains. Traditional techniques for speaker diarization developed for telephone conversations or broadcast news are based on a single channel input, which is notably different from meetings domain which can h...
متن کاملSpeaker Diarization in Meetings Domain
The purpose of this study is to develop robust techniques for speaker segmentation and clustering with focus on meetings domain. The techniques examined can however be applied to any other domains such as telephone and broadcast news. Traditional techniques for speaker diarization developed for telephone conversations or broadcast news are based on a single channel, which is notably different f...
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Audio diarization Reynolds & Carrasquillo (2005) is the process of partitioning an input audio stream into homogeneous regions according to their specific audio sources. These sources can include audio type (speech, music, background noise, ect.), speaker identity and channel characteristics. With the continually increasing number of larges volumes of spoken documents including broadcasts, voic...
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Detection of speaker, channel and environment changes in a continuous audio stream is important in various applications (e.g., broadcast news, meetings/teleconferences etc.). Standard schemes for segmentation use a classi er and hence do not generalize to unseen speaker / channel / environments. Recently S.Chen introduced new segmentation and clustering algorithms, using the so-called BIC. This...
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In this paper we describe the ICSI-SRI entry in the Rich Transcription 2005 Spring Meeting Recognition Evaluation. The current system is based on the ICSI-SRI clustering system for Broadcast News (BN), with extra modules to process the different meetings tasks in which we participated. Our base system uses agglomerative clustering with a BIC-like measure to determine when to stop merging cluste...
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