Analysis of Importance of the prosodic Features for Automatic Sentence Modality Recognition in French in real Conditions
Identifieur interne : 000E47 ( Hal/Corpus ); précédent : 000E46; suivant : 000E48Analysis of Importance of the prosodic Features for Automatic Sentence Modality Recognition in French in real Conditions
Auteurs : Pavel Kral ; Jana Kleckova ; Christophe CerisaraSource :
Descripteurs français
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Abstract
This paper deals with the measure of importance of the prosodic features for automatic sentence modality recognition in French in real conditions. We start by analysing the problem of subjectivity of manual labeling of corpus. Then, we show the results of automatic sentence modality recognition by only two prosodic features: fundamental frequency (F0) and energy. The global accuracy (ACC) is not sufficient for our application: animate a talking head for deaf and hearing-impaired children by information about the sentence type. Next, we analyse the corpus for explaining these results. We consider, that prosodic features are sufficient only for prosodic question detection with accuracy greater than 80 %. For recognition of other modalities with accuracy over 80 %, we need other informations, as language model or semantic.
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<front><div type="abstract" xml:lang="en">This paper deals with the measure of importance of the prosodic features for automatic sentence modality recognition in French in real conditions. We start by analysing the problem of subjectivity of manual labeling of corpus. Then, we show the results of automatic sentence modality recognition by only two prosodic features: fundamental frequency (F0) and energy. The global accuracy (ACC) is not sufficient for our application: animate a talking head for deaf and hearing-impaired children by information about the sentence type. Next, we analyse the corpus for explaining these results. We consider, that prosodic features are sufficient only for prosodic question detection with accuracy greater than 80 %. For recognition of other modalities with accuracy over 80 %, we need other informations, as language model or semantic.</div>
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<sourceDesc><biblStruct><analytic><title xml:lang="en">Analysis of Importance of the prosodic Features for Automatic Sentence Modality Recognition in French in real Conditions</title>
<author role="aut"><persName><forename type="first">Pavel</forename>
<surname>Kral</surname>
</persName>
<email>pkral@kiv.zcu.cz</email>
<idno type="halAuthorId">131343</idno>
<orgName ref="#struct-364475"></orgName>
<affiliation ref="#struct-2359"></affiliation>
</author>
<author role="aut"><persName><forename type="first">Jana</forename>
<surname>Kleckova</surname>
</persName>
<idno type="halAuthorId">131344</idno>
<orgName ref="#struct-364999"></orgName>
</author>
<author role="aut"><persName><forename type="first">Christophe</forename>
<surname>Cerisara</surname>
</persName>
<email>christophe.cerisara@loria.fr</email>
<idno type="idHal">christophe-cerisara</idno>
<idno type="halAuthorId">130612</idno>
<orgName ref="#struct-441569"></orgName>
<affiliation ref="#struct-2359"></affiliation>
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<monogr><idno type="localRef">A04-R-367 || kral04a</idno>
<meeting><title>WSEAS International Conference on Electronics, Control and Signal Processing - ICECS'04</title>
<date type="start">2004-11</date>
<settlement>Crete, Greece</settlement>
</meeting>
<imprint><biblScope unit="volume">3/9</biblScope>
<biblScope unit="issue">9</biblScope>
<biblScope unit="pp">1820-1824</biblScope>
<date type="datePub">2004-11</date>
</imprint>
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<profileDesc><langUsage><language ident="en">English</language>
</langUsage>
<textClass><keywords scheme="author"><term xml:lang="fr">prosodie</term>
<term xml:lang="fr">modal corpus</term>
<term xml:lang="fr">prosody</term>
<term xml:lang="fr">fundamental frequency (F0)</term>
<term xml:lang="fr">energy</term>
<term xml:lang="fr">automatic sentences modality recognition (ASMR)</term>
<term xml:lang="fr">fréquence fondamentale (F0)</term>
<term xml:lang="fr">énergie</term>
<term xml:lang="fr">reconnaissance automatique de la modalité des phrases (RAMP)</term>
<term xml:lang="fr">corpus modal</term>
</keywords>
<classCode scheme="halDomain" n="info.info-oh">Computer Science [cs]/Other [cs.OH]</classCode>
<classCode scheme="halTypology" n="COMM">Conference papers</classCode>
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<abstract xml:lang="en">This paper deals with the measure of importance of the prosodic features for automatic sentence modality recognition in French in real conditions. We start by analysing the problem of subjectivity of manual labeling of corpus. Then, we show the results of automatic sentence modality recognition by only two prosodic features: fundamental frequency (F0) and energy. The global accuracy (ACC) is not sufficient for our application: animate a talking head for deaf and hearing-impaired children by information about the sentence type. Next, we analyse the corpus for explaining these results. We consider, that prosodic features are sufficient only for prosodic question detection with accuracy greater than 80 %. For recognition of other modalities with accuracy over 80 %, we need other informations, as language model or semantic.</abstract>
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