Retrieving the syntactic structure of erroneous ASR transcriptions for open-domain Spoken Language Understanding
Identifieur interne : 000E62 ( Main/Exploration ); précédent : 000E61; suivant : 000E63Retrieving the syntactic structure of erroneous ASR transcriptions for open-domain Spoken Language Understanding
Auteurs : Frédéric Béchet [France] ; Benoit Favre [France] ; Alexis Nasr [France] ; Mathieu Morey [France]Source :
English descriptors
- mix :
Abstract
Retrieving the syntactic structure of erroneous ASR transcriptions can be of great interest for open-domain Spoken Language Understanding tasks in order to correct or at least reduce the impact of ASR errors on final applications. Most of the previous works on ASR and syntactic parsing have addressed this problem by using syntactic features during ASR to help reducing Word Error Rate (WER). The improvement obtained is rather small however the structure and the relations between words obtained through parsing can be of great interest for the SLU processes, even without a significant decrease of WER. That is why we adopt another point of view in this paper: considering that ASR transcriptions contain inevitably some errors, we show in this study that it is possible to improve the syntactic analysis of these erroneous transcriptions by performing a joint error detection / syntactic parsing process. The applicative framework used in this study is a speech-to-speech system developed through the DARPA BOLT project.
Url:
Affiliations:
- France
- Grand Est, Lorraine (région), Provence-Alpes-Côte d'Azur
- Marseille, Metz, Nancy
- Université d'Aix-Marseille, Université de Lorraine
Links toward previous steps (curation, corpus...)
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- to stream Hal, to step Curation: 004179
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- to stream Main, to step Merge: 000E57
- to stream Main, to step Curation: 000E62
Le document en format XML
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CS 25233 - 54052 Nancy cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.univ-lorraine.fr/</ref></desc></org></tutelle><tutelle name="UMR7503" active="#struct-441569" type="indirect"><org type="institution" xml:id="struct-441569" status="VALID"><idno type="ISNI">0000000122597504</idno><idno type="IdRef">02636817X</idno><orgName>Centre National de la Recherche Scientifique</orgName><orgName type="acronym">CNRS</orgName><date type="start">1939-10-19</date><desc><address><country key="FR"></country></address><ref type="url">http://www.cnrs.fr/</ref></desc></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city">Nancy</settlement><settlement type="city">Metz</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Université de Lorraine</orgName></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>Automatic Speech Recognition</term><term>Confidence Measures</term><term>Dependency Parsing</term><term>Spoken Language Understanding</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Retrieving the syntactic structure of erroneous ASR transcriptions can be of great interest for open-domain Spoken Language Understanding tasks in order to correct or at least reduce the impact of ASR errors on final applications. Most of the previous works on ASR and syntactic parsing have addressed this problem by using syntactic features during ASR to help reducing Word Error Rate (WER). The improvement obtained is rather small however the structure and the relations between words obtained through parsing can be of great interest for the SLU processes, even without a significant decrease of WER. That is why we adopt another point of view in this paper: considering that ASR transcriptions contain inevitably some errors, we show in this study that it is possible to improve the syntactic analysis of these erroneous transcriptions by performing a joint error detection / syntactic parsing process. The applicative framework used in this study is a speech-to-speech system developed through the DARPA BOLT project.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Grand Est</li><li>Lorraine (région)</li><li>Provence-Alpes-Côte d'Azur</li></region><settlement><li>Marseille</li><li>Metz</li><li>Nancy</li></settlement><orgName><li>Université d'Aix-Marseille</li><li>Université de Lorraine</li></orgName></list><tree><country name="France"><noRegion><name sortKey="Bechet, Frederic" sort="Bechet, Frederic" uniqKey="Bechet F" first="Frédéric" last="Béchet">Frédéric Béchet</name></noRegion><name sortKey="Favre, Benoit" sort="Favre, Benoit" uniqKey="Favre B" first="Benoit" last="Favre">Benoit Favre</name><name sortKey="Morey, Mathieu" sort="Morey, Mathieu" uniqKey="Morey M" first="Mathieu" last="Morey">Mathieu Morey</name><name sortKey="Nasr, Alexis" sort="Nasr, Alexis" uniqKey="Nasr A" first="Alexis" last="Nasr">Alexis Nasr</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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