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		<datafield tag="980" ind1=" " ind2=" ">
			<subfield code="a">CONF</subfield>
		</datafield>
		<datafield tag="970" ind1=" " ind2=" ">
			<subfield code="a">Jayagopi_IEEEINTERNATIONALCONFERENCEONMULTIMEDIA&amp;EXPO(ICME2009)_2009/IDIAP</subfield>
		</datafield>
		<datafield tag="245" ind1=" " ind2=" ">
			<subfield code="a">Characterising Conversationsal Group Dynamics Using Nonverbal Behaviour</subfield>
		</datafield>
		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Jayagopi, Dinesh Babu</subfield>
		</datafield>
		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Bogdan, Raducanu</subfield>
		</datafield>
		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Gatica-Perez, Daniel</subfield>
		</datafield>
		<datafield tag="856" ind1="4" ind2="0">
			<subfield code="i">EXTERNAL</subfield>
			<subfield code="u">http://publications.idiap.ch/attachments/papers/2009/Jayagopi_IEEEINTERNATIONALCONFERENCEONMULTIMEDIA&amp;EXPO(ICME2009)_2009.pdf</subfield>
			<subfield code="x">PUBLIC</subfield>
		</datafield>
		<datafield tag="711" ind1="2" ind2=" ">
			<subfield code="a">Proceedings ICME 2009</subfield>
		</datafield>
		<datafield tag="260" ind1=" " ind2=" ">
			<subfield code="c">2009</subfield>
		</datafield>
		<datafield tag="771" ind1="2" ind2=" ">
			<subfield code="d">June 2009</subfield>
		</datafield>
		<datafield tag="520" ind1=" " ind2=" ">
			<subfield code="a">This paper addresses the novel problemof characterizing conversational
group dynamics. It is well documented in social
psychology that depending on the objectives a group, the dynamics
are different. For example, a competitive meeting has
a different objective from that of a collaborative meeting. We
propose a method to characterize group dynamics based on
the joint description of a group membersâ€™ aggregated acoustical
nonverbal behaviour to classify two meeting datasets (one
being cooperative-type and the other being competitive-type).
We use 4.5 hours of real behavioural multi-party data and
show that our methodology can achieve a classification rate
of upto 100%.</subfield>
		</datafield>
	</record>
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