The Importance of Segmentation Probability in Segment Based Speech Recognizers
Identifieur interne : 00C093 ( Main/Merge ); précédent : 00C092; suivant : 00C094The Importance of Segmentation Probability in Segment Based Speech Recognizers
Auteurs : Jan Verhasselt ; Irina Illina ; Jean-Pierre Martens ; Yifan Gong ; Jean-Paul Haton [France]Source :
English descriptors
Abstract
In segment based recognizers, variable length speech segments are mapped to the basic speech units (phones, diphones, ...). In this paper, we address the acoustical modeling of these basic units in the framework of segmental posterior distribution models (SPDM). The joint posterior probability of a unit sequence \underline{u} and a segmentation \underline{s}, Pr(\underline{u},\underline{s}|\underline{\bf x}) can be written as the product of the segmentation probability Pr(\underline{s}|\underline{bf x}) and the unit classification probability Pr(\underline{u}|\underline{s},\underline{\bf x}), where \underline{\bf x} is the sequence of acoustic observation parameter vectors. In particular, we point out the role of the segmentation probability and demonstrate that it does improve the recognition accuracy. We present evidence for this in two different tasks (speaker dependent continuous word recognition in French and speaker independent phone recognition in American English) in combination with two different unit classification models.
Links toward previous steps (curation, corpus...)
- to stream Crin, to step Corpus: 001E88
- to stream Crin, to step Curation: 001E88
- to stream Crin, to step Checkpoint: 002639
Links to Exploration step
CRIN:verhasselt97bLe document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en" wicri:score="492">The Importance of Segmentation Probability in Segment Based Speech Recognizers</title></titleStmt><publicationStmt><idno type="RBID">CRIN:verhasselt97b</idno><date when="1997" year="1997">1997</date><idno type="wicri:Area/Crin/Corpus">001E88</idno><idno type="wicri:Area/Crin/Curation">001E88</idno><idno type="wicri:explorRef" wicri:stream="Crin" wicri:step="Curation">001E88</idno><idno type="wicri:Area/Crin/Checkpoint">002639</idno><idno type="wicri:explorRef" wicri:stream="Crin" wicri:step="Checkpoint">002639</idno><idno type="wicri:Area/Main/Merge">00C093</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">The Importance of Segmentation Probability in Segment Based Speech Recognizers</title><author><name sortKey="Verhasselt, Jan" sort="Verhasselt, Jan" uniqKey="Verhasselt J" first="Jan" last="Verhasselt">Jan Verhasselt</name></author><author><name sortKey="Illina, Irina" sort="Illina, Irina" uniqKey="Illina I" first="Irina" last="Illina">Irina Illina</name></author><author><name sortKey="Martens, Jean Pierre" sort="Martens, Jean Pierre" uniqKey="Martens J" first="Jean-Pierre" last="Martens">Jean-Pierre Martens</name></author><author><name sortKey="Gong, Yifan" sort="Gong, Yifan" uniqKey="Gong Y" first="Yifan" last="Gong">Yifan Gong</name></author><author><name sortKey="Haton, Jean Paul" sort="Haton, Jean Paul" uniqKey="Haton J" first="Jean-Paul" last="Haton">Jean-Paul Haton</name><affiliation><country>France</country><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="region" nuts="2">Lorraine (région)</region></placeName><orgName type="laboratoire" n="5">Laboratoire lorrain de recherche en informatique et ses applications</orgName><orgName type="university">Université de Lorraine</orgName><orgName type="institution">Centre national de la recherche scientifique</orgName><orgName type="institution">Institut national de recherche en informatique et en automatique</orgName></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>segment-based recognizer</term><term>speech recognition</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en" wicri:score="2697">In segment based recognizers, variable length speech segments are mapped to the basic speech units (phones, diphones, ...). In this paper, we address the acoustical modeling of these basic units in the framework of segmental posterior distribution models (SPDM). The joint posterior probability of a unit sequence \underline{u} and a segmentation \underline{s}, Pr(\underline{u},\underline{s}|\underline{\bf x}) can be written as the product of the segmentation probability Pr(\underline{s}|\underline{bf x}) and the unit classification probability Pr(\underline{u}|\underline{s},\underline{\bf x}), where \underline{\bf x} is the sequence of acoustic observation parameter vectors. In particular, we point out the role of the segmentation probability and demonstrate that it does improve the recognition accuracy. We present evidence for this in two different tasks (speaker dependent continuous word recognition in French and speaker independent phone recognition in American English) in combination with two different unit classification models.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Lorraine/explor/InforLorV4/Data/Main/Merge
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 00C093 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Merge/biblio.hfd -nk 00C093 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Wicri/Lorraine
|area= InforLorV4
|flux= Main
|étape= Merge
|type= RBID
|clé= CRIN:verhasselt97b
|texte= The Importance of Segmentation Probability in Segment Based Speech Recognizers
}}
| This area was generated with Dilib version V0.6.33. |  |