InforLorV4, Hal, Corpus, bibRecord, 000E47

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 : 000E48

Analysis of Importance of the prosodic Features for Automatic Sentence Modality Recognition in French in real Conditions

Auteurs : Pavel Kral ; Jana Kleckova ; Christophe Cerisara

Source :

RBID : Hal:inria-00100102

Descripteurs français

mix :
- automatic sentences modality recognition (ASMR), corpus modal, energy, fréquence fondamentale (F0), fundamental frequency (F0), modal corpus, prosodie, prosody, reconnaissance automatique de la modalité des phrases (RAMP), énergie.

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.

Url:

https://hal.inria.fr/inria-00100102

Links to Exploration step

Hal:inria-00100102

Le document en format XML

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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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<idno type="halRefHtml">WSEAS International Conference on Electronics, Control and Signal Processing - ICECS'04, Nov 2004, Crete, Greece, 3/9 (9), pp.1820-1824, 2004</idno>
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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>
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<email>pkral@kiv.zcu.cz</email>
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<author role="aut"><persName><forename type="first">Jana</forename>
<surname>Kleckova</surname>
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<idno type="halAuthorId">131344</idno>
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<author role="aut"><persName><forename type="first">Christophe</forename>
<surname>Cerisara</surname>
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<email>christophe.cerisara@loria.fr</email>
<idno type="idHal">christophe-cerisara</idno>
<idno type="halAuthorId">130612</idno>
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<affiliation ref="#struct-2359"></affiliation>
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<meeting><title>WSEAS International Conference on Electronics, Control and Signal Processing - ICECS'04</title>
<date type="start">2004-11</date>
<settlement>Crete, Greece</settlement>
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<imprint><biblScope unit="volume">3/9</biblScope>
<biblScope unit="issue">9</biblScope>
<biblScope unit="pp">1820-1824</biblScope>
<date type="datePub">2004-11</date>
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<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>
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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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Analysis of Importance of the prosodic Features for Automatic Sentence Modality Recognition in French in real Conditions

Analysis of Importance of the prosodic Features for Automatic Sentence Modality Recognition in French in real Conditions

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