Comparison and Analysis of Several Phonetic Decoding Approaches
Identifieur interne : 000F41 ( Hal/Checkpoint ); précédent : 000F40; suivant : 000F42Comparison and Analysis of Several Phonetic Decoding Approaches
Auteurs : Luiza Orosanu [France] ; Denis Jouvet [France]Source :
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Abstract
This article analyzes the phonetic decoding performance obtained with different choices of linguistic units. The context is to later use such an approach as a support for helping communication with deaf people, and to run it on an embedded decoder on a portable terminal, which introduces constrains on the model size. As a first step, this paper compares the performance of various approaches on the ESTER2 and ETAPE speech corpora. Two baseline systems are considered, one relying on a large vocabulary speech recognizer, and another one relying on a phonetic n-gram language model. The third model which relies on a syllable-based lexicon and a trigram language model, provides a good tradeoff between model size and phonetic decoding performance. The phone error rate is only 4% worse (absolute) than the phone error rate obtained with the large vocabulary recognizer, and much better than the phone error rate obtained with the phone n-gram language model. Phone error rates are then analyzed with respect to SNR and speaking rate.
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The context is to later use such an approach as a support for helping communication with deaf people, and to run it on an embedded decoder on a portable terminal, which introduces constrains on the model size. As a first step, this paper compares the performance of various approaches on the ESTER2 and ETAPE speech corpora. Two baseline systems are considered, one relying on a large vocabulary speech recognizer, and another one relying on a phonetic n-gram language model. The third model which relies on a syllable-based lexicon and a trigram language model, provides a good tradeoff between model size and phonetic decoding performance. The phone error rate is only 4% worse (absolute) than the phone error rate obtained with the large vocabulary recognizer, and much better than the phone error rate obtained with the phone n-gram language model. Phone error rates are then analyzed with respect to SNR and speaking rate.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Comparison and Analysis of Several Phonetic Decoding Approaches</title><author role="aut"><persName><forename type="first">Luiza</forename><surname>Orosanu</surname></persName><email></email><idno type="halauthor">784816</idno><affiliation ref="#struct-205127"></affiliation></author><author role="aut"><persName><forename type="first">Denis</forename><surname>Jouvet</surname></persName><email>denis.jouvet@inria.fr</email><idno type="halauthor">535851</idno><orgName ref="#struct-364771"></orgName><affiliation ref="#struct-205127"></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">2016-03-25 17:35:58</date><date type="whenModified">2016-03-30 01:07:31</date><date type="whenReleased">2016-03-29 11:42:11</date><date type="whenProduced">2013-09-01</date><date type="whenEndEmbargoed">2016-03-25</date><ref type="file" target="https://hal.inria.fr/hal-00834313/document"><date notBefore="2016-03-25"></date></ref><ref type="file" subtype="author" n="1" target="https://hal.inria.fr/hal-00834313/file/articleTSD2013_%2398-Luiza-Orosanu-final.pdf"><date notBefore="2016-03-25"></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-00834313</idno><idno type="halUri">https://hal.inria.fr/hal-00834313</idno><idno type="halBibtex">orosanu:hal-00834313</idno><idno type="halRefHtml">Ivan Habernal and Václav Matoušek. TSD - 16th International Conference on Text, Speech and Dialogue - 2013, Sep 2013, Pilsen, Czech Republic. Springer Verlag, 8082, pp.161-168, 2013, Lecture Notes in Artificial Intelligence. <http://link.springer.com/chapter/10.1007%2F978-3-642-40585-3_21></idno><idno type="halRef">Ivan Habernal and Václav Matoušek. TSD - 16th International Conference on Text, Speech and Dialogue - 2013, Sep 2013, Pilsen, Czech Republic. Springer Verlag, 8082, pp.161-168, 2013, Lecture Notes in Artificial Intelligence. <http://link.springer.com/chapter/10.1007%2F978-3-642-40585-3_21></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="LORIA-TALC" p="LORIA">Traitement automatique des langues et des connaissances</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</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><idno type="stamp" n="INRIA2">INRIA 2</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></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">Comparison and Analysis of Several Phonetic Decoding Approaches</title><author role="aut"><persName><forename type="first">Luiza</forename><surname>Orosanu</surname></persName><idno type="halAuthorId">784816</idno><affiliation ref="#struct-205127"></affiliation></author><author role="aut"><persName><forename type="first">Denis</forename><surname>Jouvet</surname></persName><email>denis.jouvet@inria.fr</email><idno type="halAuthorId">535851</idno><orgName ref="#struct-364771"></orgName><affiliation ref="#struct-205127"></affiliation></author></analytic><monogr><meeting><title>TSD - 16th International Conference on Text, Speech and Dialogue - 2013</title><date type="start">2013-09-01</date><date type="end">2013-09-05</date><settlement>Pilsen</settlement><country key="CZ">Czech Republic</country></meeting><editor>Ivan Habernal and Václav Matoušek</editor><imprint><publisher>Springer Verlag</publisher><biblScope unit="serie">Lecture Notes in Artificial Intelligence</biblScope><biblScope unit="volume">8082</biblScope><biblScope unit="pp">161-168</biblScope><date type="datePub">2013-09-01</date></imprint></monogr><ref type="publisher">http://link.springer.com/chapter/10.1007%2F978-3-642-40585-3_21</ref></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><keywords scheme="author"><term xml:lang="en">syllables</term><term xml:lang="en">deaf</term><term xml:lang="en">speech recognition</term><term xml:lang="en">embedded system</term></keywords><classCode scheme="halDomain" n="info.info-ts">Computer Science [cs]/Signal and Image Processing</classCode><classCode scheme="halDomain" n="spi.signal">Engineering Sciences [physics]/Signal and Image processing</classCode><classCode scheme="halTypology" n="COMM">Conference papers</classCode></textClass><abstract xml:lang="en">This article analyzes the phonetic decoding performance obtained with different choices of linguistic units. The context is to later use such an approach as a support for helping communication with deaf people, and to run it on an embedded decoder on a portable terminal, which introduces constrains on the model size. As a first step, this paper compares the performance of various approaches on the ESTER2 and ETAPE speech corpora. Two baseline systems are considered, one relying on a large vocabulary speech recognizer, and another one relying on a phonetic n-gram language model. The third model which relies on a syllable-based lexicon and a trigram language model, provides a good tradeoff between model size and phonetic decoding performance. The phone error rate is only 4% worse (absolute) than the phone error rate obtained with the large vocabulary recognizer, and much better than the phone error rate obtained with the phone n-gram language model. Phone error rates are then analyzed with respect to SNR and speaking rate.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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