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			<subfield code="a">REPORT</subfield>
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			<subfield code="a">lapidot-rr02-60/IDIAP</subfield>
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		<datafield tag="245" ind1=" " ind2=" ">
			<subfield code="a">Self-Organizing-Maps With BIC For Speaker Clustering</subfield>
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		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Lapidot, I.</subfield>
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		<datafield tag="856" ind1="4" ind2="0">
			<subfield code="i">EXTERNAL</subfield>
			<subfield code="u">http://publications.idiap.ch/attachments/reports/2002/rr02-60.pdf</subfield>
			<subfield code="x">PUBLIC</subfield>
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		<datafield tag="088" ind1=" " ind2=" ">
			<subfield code="a">Idiap-RR-60-2002</subfield>
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			<subfield code="c">2002</subfield>
			<subfield code="b">IDIAP</subfield>
			<subfield code="a">Martigny, Switzerland</subfield>
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		<datafield tag="520" ind1=" " ind2=" ">
			<subfield code="a">A new approach is presented for clustering the speakers from unlabeled and unsegmented conversation, when the number of speakers is unknown. In this approach, each speaker is modeled by a Self- Organizing-Map (SOM). For estimation of the number of clusters the Bayesian Information Criterion (BIC) is applied. This approach was tested on the NIST 1996 HUB-4 evaluation test in terms of speaker and cluster purities. Results indicate that the combined SOM-BIC approach can lead to better clustering results than the baseline system.</subfield>
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