Detection of sentence modality on French automatic speech-to-text transcriptions
Identifieur interne : 001C19 ( Hal/Curation ); précédent : 001C18; suivant : 001C20Detection of sentence modality on French automatic speech-to-text transcriptions
Auteurs : Luiza Orosanu [France] ; Denis Jouvet [France]Source :
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Abstract
This article analyzes the detection of sentence modality in French when it is applied on automatic speech-to-text transcriptions. Two sentence modalities are evaluated (questions and statements) using prosodic and linguistic information. The linguistic features consider the presence of discriminative interrogative patterns and two log-likelihood ratios of the sentence being a question rather than a statement: one based on words and the other one based on part-of-speech tags. The prosodic features are based on duration, energy and pitch features estimated over the last prosodic group of the sentence. The classifiers based on linguistic features outperform the classifiers based on prosodic features. The combination of linguistic and prosodic features gives a slight improvement on automatic speech transcriptions, where the correct classification performance reaches 72%. A detailed analysis shows that small errors in the determination of the segment boundaries are not critical.
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Two sentence modalities are evaluated (questions and statements) using prosodic and linguistic information. The linguistic features consider the presence of discriminative interrogative patterns and two log-likelihood ratios of the sentence being a question rather than a statement: one based on words and the other one based on part-of-speech tags. The prosodic features are based on duration, energy and pitch features estimated over the last prosodic group of the sentence. The classifiers based on linguistic features outperform the classifiers based on prosodic features. The combination of linguistic and prosodic features gives a slight improvement on automatic speech transcriptions, where the correct classification performance reaches 72%. A detailed analysis shows that small errors in the determination of the segment boundaries are not critical.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Detection of sentence modality on French automatic speech-to-text transcriptions</title><author role="aut"><persName><forename type="first">Luiza</forename><surname>Orosanu</surname></persName><email></email><idno type="halauthor">784816</idno><affiliation ref="#struct-420403"></affiliation></author><author role="aut"><persName><forename type="first">Denis</forename><surname>Jouvet</surname></persName><email></email><idno type="halauthor">656680</idno><affiliation ref="#struct-420403"></affiliation></author><editor role="depositor"><persName><forename>Denis</forename><surname>Jouvet</surname></persName><email>denis.jouvet@inria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2015-08-13 11:20:06</date><date type="whenModified">2015-09-21 11:16:03</date><date type="whenReleased">2015-08-13 15:42:40</date><date type="whenProduced">2015-10-18</date><date type="whenEndEmbargoed">2015-08-13</date><ref type="file" target="https://hal.inria.fr/hal-01184193/document"><date notBefore="2015-08-13"></date></ref><ref type="file" subtype="author" n="1" target="https://hal.inria.fr/hal-01184193/file/articleICNLSP2015-QD-final.pdf"><date notBefore="2015-08-13"></date></ref></edition><respStmt><resp>contributor</resp><name key="155601"><persName><forename>Denis</forename><surname>Jouvet</surname></persName><email>denis.jouvet@inria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">hal-01184193</idno><idno type="halUri">https://hal.inria.fr/hal-01184193</idno><idno type="halBibtex">orosanu:hal-01184193</idno><idno type="halRefHtml">International Conference on Natural Language and Speech Processing, Oct 2015, Alger, Algeria. Proceedings ICNLSP'2015, International Conference on Natural Language and Speech Processing</idno><idno type="halRef">International Conference on Natural Language and Speech Processing, Oct 2015, Alger, Algeria. Proceedings ICNLSP'2015, International Conference on Natural Language and Speech Processing</idno></publicationStmt><seriesStmt><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="INRIA-LORRAINE">INRIA Nancy - Grand Est</idno><idno type="stamp" n="INRIA-NANCY-GRAND-EST">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="INRIA_TEST">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno></seriesStmt><notesStmt><note type="audience" n="2">International</note><note type="invited" n="0">No</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note><note type="proceedings" n="1">Yes</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Detection of sentence modality on French automatic speech-to-text transcriptions</title><author role="aut"><persName><forename type="first">Luiza</forename><surname>Orosanu</surname></persName><idno type="halAuthorId">784816</idno><affiliation ref="#struct-420403"></affiliation></author><author role="aut"><persName><forename type="first">Denis</forename><surname>Jouvet</surname></persName><idno type="halAuthorId">656680</idno><affiliation ref="#struct-420403"></affiliation></author></analytic><monogr><title level="m">Proceedings ICNLSP'2015, International Conference on Natural Language and Speech Processing</title><meeting><title>International Conference on Natural Language and Speech Processing</title><date type="start">2015-10-18</date><date type="end">2015-10-19</date><settlement>Alger</settlement><country key="DZ">Algeria</country></meeting><imprint></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><keywords scheme="author"><term xml:lang="en">question detection</term><term xml:lang="en">speech-to-text transcriptions</term><term xml:lang="en">prosody</term><term xml:lang="en">likelihood ratio</term><term xml:lang="en">part-of-speech tags</term></keywords><classCode scheme="halDomain" n="info.info-ts">Computer Science [cs]/Signal and Image Processing</classCode><classCode scheme="halTypology" n="COMM">Conference papers</classCode></textClass><abstract xml:lang="en">This article analyzes the detection of sentence modality in French when it is applied on automatic speech-to-text transcriptions. Two sentence modalities are evaluated (questions and statements) using prosodic and linguistic information. The linguistic features consider the presence of discriminative interrogative patterns and two log-likelihood ratios of the sentence being a question rather than a statement: one based on words and the other one based on part-of-speech tags. The prosodic features are based on duration, energy and pitch features estimated over the last prosodic group of the sentence. The classifiers based on linguistic features outperform the classifiers based on prosodic features. The combination of linguistic and prosodic features gives a slight improvement on automatic speech transcriptions, where the correct classification performance reaches 72%. A detailed analysis shows that small errors in the determination of the segment boundaries are not critical.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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