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			<subfield code="a">REPORT</subfield>
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			<subfield code="a">hynek-rr-03-50/IDIAP</subfield>
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		<datafield tag="245" ind1=" " ind2=" ">
			<subfield code="a">TRAP-TANDEM: Data-driven extraction of temporal features from speech</subfield>
		</datafield>
		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Hermansky, Hynek</subfield>
		</datafield>
		<datafield tag="856" ind1="4" ind2="0">
			<subfield code="i">EXTERNAL</subfield>
			<subfield code="u">http://publications.idiap.ch/attachments/reports/2003/rr03-50.pdf</subfield>
			<subfield code="x">PUBLIC</subfield>
		</datafield>
		<datafield tag="088" ind1=" " ind2=" ">
			<subfield code="a">Idiap-RR-50-2003</subfield>
		</datafield>
		<datafield tag="260" ind1=" " ind2=" ">
			<subfield code="c">2003</subfield>
			<subfield code="b">IDIAP</subfield>
			<subfield code="a">Martigny, Switzerland</subfield>
		</datafield>
		<datafield tag="500" ind1=" " ind2=" ">
			<subfield code="a">large part published in Proceedings of ASRU-2003</subfield>
		</datafield>
		<datafield tag="520" ind1=" " ind2=" ">
			<subfield code="a">Conventional features in automatic recognition of speech describe instantaneous shape of a short-time spectrum of speech. The TRAP-TANDEM features describe likelihoods of sub-word classess at a given time instant, derived from temporal trajectories of band-limited spectral densities in the vicinity of a given time instant. The paper presents some rationale behind the data-driven TRAP-TANDEM approach, briefly describes the technique, point to relevant oublications and summarizes results achieved so far.</subfield>
		</datafield>
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