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Combination of agglomerative and sequential clustering for speaker diarization
This paper aims at investigating the use of sequential clustering for speaker diarization. Conventional diarization systems are based on parametric models and agglomerative clustering. In our previous work we proposed a non-parametric method based on the agglomerative information bottleneck for very...
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| creator | Vijayasenan, D. Valente, F. Bourlard, H. |
| description | This paper aims at investigating the use of sequential clustering for speaker diarization. Conventional diarization systems are based on parametric models and agglomerative clustering. In our previous work we proposed a non-parametric method based on the agglomerative information bottleneck for very fast diarization. Here we consider the combination of sequential and agglomerative clustering for avoiding local maxima of the objective function and for purification. Experiments are run on the RT06 eval data. Sequential Clustering with oracle model selection can reduce the speaker error by 10% w.r.t. agglomerative clustering. When the model selection is based on Normalized Mutual Information criterion, a relative improvement of 5% is obtained using a combination of agglomerative and sequential clustering. |
| doi_str_mv | 10.1109/ICASSP.2008.4518621 |
| format | conference_proceeding |
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Conventional diarization systems are based on parametric models and agglomerative clustering. In our previous work we proposed a non-parametric method based on the agglomerative information bottleneck for very fast diarization. Here we consider the combination of sequential and agglomerative clustering for avoiding local maxima of the objective function and for purification. Experiments are run on the RT06 eval data. Sequential Clustering with oracle model selection can reduce the speaker error by 10% w.r.t. agglomerative clustering. 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Conventional diarization systems are based on parametric models and agglomerative clustering. In our previous work we proposed a non-parametric method based on the agglomerative information bottleneck for very fast diarization. Here we consider the combination of sequential and agglomerative clustering for avoiding local maxima of the objective function and for purification. Experiments are run on the RT06 eval data. Sequential Clustering with oracle model selection can reduce the speaker error by 10% w.r.t. agglomerative clustering. When the model selection is based on Normalized Mutual Information criterion, a relative improvement of 5% is obtained using a combination of agglomerative and sequential clustering.</description><subject>agglomerative and sequential information bottleneck</subject><subject>Bayesian methods</subject><subject>Clustering algorithms</subject><subject>Hidden Markov models</subject><subject>Meetings data</subject><subject>Mutual information</subject><subject>Parameter estimation</subject><subject>Parametric statistics</subject><subject>Partitioning algorithms</subject><subject>Purification</subject><subject>Speaker Diarization</subject><subject>Speech</subject><subject>Streaming media</subject><issn>1520-6149</issn><issn>2379-190X</issn><isbn>9781424414833</isbn><isbn>1424414830</isbn><isbn>1424414849</isbn><isbn>9781424414840</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2008</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNo1kNtKxDAYhOMJrOs-wd7kBVrz59Aml1J0FRYVVsG75W-alGgPa9oV9OlddJ2bgRn4YIaQBbAMgJmr-_J6vX7KOGM6kwp0zuGIXIDkUoLU0hyThIvCpGDY6wmZm0L_d0KckgQUZ2kO0pyT-Ti-sb2kEsqohDyUQ1eFHqcw9HTwFJumHToX98Gno9jXdHQfO9dPAVtq2904uRj6hvoh0nHr8N1FWgeM4fsXcUnOPLajmx98Rl5ub57Lu3T1uNyPWKWBczalACgs0xVaEEWFtc9rXXmJ3oA3XuQ1175CwYXikjMsfG0lKCHBgeXMWjEjiz9ucM5ttjF0GL82h2vED85yVYI</recordid><startdate>20080101</startdate><enddate>20080101</enddate><creator>Vijayasenan, D.</creator><creator>Valente, F.</creator><creator>Bourlard, H.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>20080101</creationdate><title>Combination of agglomerative and sequential clustering for speaker diarization</title><author>Vijayasenan, D. ; Valente, F. ; Bourlard, H.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i220t-11a3c08bac137badf6d8bf4af91f9f36d28fba32352420a7fdc415341e1c20cc3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2008</creationdate><topic>agglomerative and sequential information bottleneck</topic><topic>Bayesian methods</topic><topic>Clustering algorithms</topic><topic>Hidden Markov models</topic><topic>Meetings data</topic><topic>Mutual information</topic><topic>Parameter estimation</topic><topic>Parametric statistics</topic><topic>Partitioning algorithms</topic><topic>Purification</topic><topic>Speaker Diarization</topic><topic>Speech</topic><topic>Streaming media</topic><toplevel>online_resources</toplevel><creatorcontrib>Vijayasenan, D.</creatorcontrib><creatorcontrib>Valente, F.</creatorcontrib><creatorcontrib>Bourlard, H.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEL</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Vijayasenan, D.</au><au>Valente, F.</au><au>Bourlard, H.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Combination of agglomerative and sequential clustering for speaker diarization</atitle><btitle>2008 IEEE International Conference on Acoustics, Speech and Signal Processing</btitle><stitle>ICASSP</stitle><date>2008-01-01</date><risdate>2008</risdate><spage>4361</spage><epage>4364</epage><pages>4361-4364</pages><issn>1520-6149</issn><eissn>2379-190X</eissn><isbn>9781424414833</isbn><isbn>1424414830</isbn><eisbn>1424414849</eisbn><eisbn>9781424414840</eisbn><abstract>This paper aims at investigating the use of sequential clustering for speaker diarization. Conventional diarization systems are based on parametric models and agglomerative clustering. In our previous work we proposed a non-parametric method based on the agglomerative information bottleneck for very fast diarization. Here we consider the combination of sequential and agglomerative clustering for avoiding local maxima of the objective function and for purification. Experiments are run on the RT06 eval data. Sequential Clustering with oracle model selection can reduce the speaker error by 10% w.r.t. agglomerative clustering. When the model selection is based on Normalized Mutual Information criterion, a relative improvement of 5% is obtained using a combination of agglomerative and sequential clustering.</abstract><pub>IEEE</pub><doi>10.1109/ICASSP.2008.4518621</doi><tpages>4</tpages><oa>free_for_read</oa></addata></record> |
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| ispartof | 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008, p.4361-4364 |
| issn | 1520-6149 2379-190X |
| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | agglomerative and sequential information bottleneck Bayesian methods Clustering algorithms Hidden Markov models Meetings data Mutual information Parameter estimation Parametric statistics Partitioning algorithms Purification Speaker Diarization Speech Streaming media |
| title | Combination of agglomerative and sequential clustering for speaker diarization |
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