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		<datafield tag="980" ind1=" " ind2=" ">
			<subfield code="a">CONF</subfield>
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		<datafield tag="970" ind1=" " ind2=" ">
			<subfield code="a">Khoury_NISTSRE_2012/IDIAP</subfield>
		</datafield>
		<datafield tag="245" ind1=" " ind2=" ">
			<subfield code="a">The Idiap Speaker Recognition Evaluation System at NIST SRE 2012</subfield>
		</datafield>
		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Khoury, Elie</subfield>
		</datafield>
		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">El Shafey, Laurent</subfield>
		</datafield>
		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Marcel, Sébastien</subfield>
		</datafield>
		<datafield tag="711" ind1="2" ind2=" ">
			<subfield code="a">NIST - NIST Speaker Recognition Conference</subfield>
			<subfield code="c">Orlando, USA</subfield>
		</datafield>
		<datafield tag="260" ind1=" " ind2=" ">
			<subfield code="c">2012</subfield>
		</datafield>
		<datafield tag="520" ind1=" " ind2=" ">
			<subfield code="a">In this paper, we present the Idiap Research Institute submission to the 2012 NIST Speaker Recognition Evaluation. Our system is based on the Inter-Session Variability
(ISV) modelling technique. The implementation of the system relies on Bob, a free signal processing and machine learning toolbox developed at Idiap. The NIST official results show the effectiveness of the proposed approach, especially on added noise
data.</subfield>
		</datafield>
	</record>
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