Speaker clustering using vector quantization and spectral clustering
We present a speaker clustering method for conversational speech recordings that contain short utterances from multiple speakers. The proposed method represents a speech segment with a vector of VQ code frequencies and uses a cosine between two vectors as their similarity measure. The clustering is...
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description | We present a speaker clustering method for conversational speech recordings that contain short utterances from multiple speakers. The proposed method represents a speech segment with a vector of VQ code frequencies and uses a cosine between two vectors as their similarity measure. The clustering is performed by a spectral clustering algorithm with cluster number estimation based on an eigen structure of the similarity matrix. We conducted experiments on five test sets with different utterance length distributions to compare the proposed method with the conventional approach based on a hierarchical agglomerative clustering using BIC stopping criterion. The results show that the proposed method significantly outperforms the conventional one in speaker diarization error rate and purity metrics. |
doi_str_mv | 10.1109/ICASSP.2010.5495078 |
format | Conference Proceeding |
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The proposed method represents a speech segment with a vector of VQ code frequencies and uses a cosine between two vectors as their similarity measure. The clustering is performed by a spectral clustering algorithm with cluster number estimation based on an eigen structure of the similarity matrix. We conducted experiments on five test sets with different utterance length distributions to compare the proposed method with the conventional approach based on a hierarchical agglomerative clustering using BIC stopping criterion. The results show that the proposed method significantly outperforms the conventional one in speaker diarization error rate and purity metrics.</description><identifier>ISSN: 1520-6149</identifier><identifier>ISBN: 9781424442959</identifier><identifier>ISBN: 1424442958</identifier><identifier>EISSN: 2379-190X</identifier><identifier>EISBN: 9781424442966</identifier><identifier>EISBN: 1424442966</identifier><identifier>DOI: 10.1109/ICASSP.2010.5495078</identifier><language>eng</language><publisher>IEEE</publisher><subject>Bayesian information criterion ; Bayesian methods ; Broadcasting ; Clustering algorithms ; Clustering methods ; Frequency ; hierarchical agglomerative clustering ; Poles and towers ; Robustness ; speaker clustering ; spectral clustering ; Speech ; Testing ; Vector quantization</subject><ispartof>2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010, p.4986-4989</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5495078$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5495078$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Iso, K</creatorcontrib><title>Speaker clustering using vector quantization and spectral clustering</title><title>2010 IEEE International Conference on Acoustics, Speech and Signal Processing</title><addtitle>ICASSP</addtitle><description>We present a speaker clustering method for conversational speech recordings that contain short utterances from multiple speakers. The proposed method represents a speech segment with a vector of VQ code frequencies and uses a cosine between two vectors as their similarity measure. The clustering is performed by a spectral clustering algorithm with cluster number estimation based on an eigen structure of the similarity matrix. We conducted experiments on five test sets with different utterance length distributions to compare the proposed method with the conventional approach based on a hierarchical agglomerative clustering using BIC stopping criterion. The results show that the proposed method significantly outperforms the conventional one in speaker diarization error rate and purity metrics.</description><subject>Bayesian information criterion</subject><subject>Bayesian methods</subject><subject>Broadcasting</subject><subject>Clustering algorithms</subject><subject>Clustering methods</subject><subject>Frequency</subject><subject>hierarchical agglomerative clustering</subject><subject>Poles and towers</subject><subject>Robustness</subject><subject>speaker clustering</subject><subject>spectral clustering</subject><subject>Speech</subject><subject>Testing</subject><subject>Vector quantization</subject><issn>1520-6149</issn><issn>2379-190X</issn><isbn>9781424442959</isbn><isbn>1424442958</isbn><isbn>9781424442966</isbn><isbn>1424442966</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVUM1Kw0AYXP_AWPsEveQFUvfv22SPUq0KBYX04K18Zr-V1ZjG3VTQpzdiD3qZgRlmGIaxmeBzIbi9uFtc1vXDXPJRAG2Bl9UBm9qyElpqraU15pBlUpW2EJY_Hv3zwB6zTIDkhRHanrKzlF4451Wpq4xd1T3hK8W8aXdpoBi653yXfvCDmmEb8_cddkP4wiFsuxw7l6d-NCK2fxLn7MRjm2i65wlbL6_Xi9tidX8zTl8VQZQwFAaBlMOSG7KgHRllyYFz-KTQCwAkQm608t4Y6bGxmoMAZYxrqkZ5NWGz39pARJs-hjeMn5v9H-obBZ1SEg</recordid><startdate>201003</startdate><enddate>201003</enddate><creator>Iso, K</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>201003</creationdate><title>Speaker clustering using vector quantization and spectral clustering</title><author>Iso, K</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-6a5e3da706e954de639ed5ddab3af155aeea0643ff662fac940515366dc8c3f3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Bayesian information criterion</topic><topic>Bayesian methods</topic><topic>Broadcasting</topic><topic>Clustering algorithms</topic><topic>Clustering methods</topic><topic>Frequency</topic><topic>hierarchical agglomerative clustering</topic><topic>Poles and towers</topic><topic>Robustness</topic><topic>speaker clustering</topic><topic>spectral clustering</topic><topic>Speech</topic><topic>Testing</topic><topic>Vector quantization</topic><toplevel>online_resources</toplevel><creatorcontrib>Iso, K</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>IEEE Electronic Library (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>Iso, K</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Speaker clustering using vector quantization and spectral clustering</atitle><btitle>2010 IEEE International Conference on Acoustics, Speech and Signal Processing</btitle><stitle>ICASSP</stitle><date>2010-03</date><risdate>2010</risdate><spage>4986</spage><epage>4989</epage><pages>4986-4989</pages><issn>1520-6149</issn><eissn>2379-190X</eissn><isbn>9781424442959</isbn><isbn>1424442958</isbn><eisbn>9781424442966</eisbn><eisbn>1424442966</eisbn><abstract>We present a speaker clustering method for conversational speech recordings that contain short utterances from multiple speakers. The proposed method represents a speech segment with a vector of VQ code frequencies and uses a cosine between two vectors as their similarity measure. The clustering is performed by a spectral clustering algorithm with cluster number estimation based on an eigen structure of the similarity matrix. We conducted experiments on five test sets with different utterance length distributions to compare the proposed method with the conventional approach based on a hierarchical agglomerative clustering using BIC stopping criterion. The results show that the proposed method significantly outperforms the conventional one in speaker diarization error rate and purity metrics.</abstract><pub>IEEE</pub><doi>10.1109/ICASSP.2010.5495078</doi><tpages>4</tpages></addata></record> |
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subjects | Bayesian information criterion Bayesian methods Broadcasting Clustering algorithms Clustering methods Frequency hierarchical agglomerative clustering Poles and towers Robustness speaker clustering spectral clustering Speech Testing Vector quantization |
title | Speaker clustering using vector quantization and spectral clustering |
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