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Neural Networks for Proper Name Retrieval in the Framework of Automatic Speech Recognition

Identifieur interne : 000799 ( Main/Exploration ); précédent : 000798; suivant : 000800

Neural Networks for Proper Name Retrieval in the Framework of Automatic Speech Recognition

Auteurs : Dominique Fohr [France] ; Irina Illina [France]

Source :

RBID : Hal:hal-01184957

English descriptors

Abstract

The problem of out-of-vocabulary words, more precisely proper names retrieval for in speech recognition is investigated. Speech recognition vocabulary is extended using diachronic documents. This article explores a new method based on neural network (NN), proposed recently by Mikolov. The NN uses high-quality continuous representation of words from large amounts of unstructured text data and predicts surrounding words of one input word. Different strategies of using the NN to take into account lexical context are proposed. Experimental results on broadcast speech recognition and comparison with previously proposed methods show an ability of NN representation to model semantic and lexical context of proper names.

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Le document en format XML

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<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>neural networks</term>
<term>out-of-vocabulary words</term>
<term>proper names</term>
<term>speech recognition</term>
<term>vocabulary extension</term>
</keywords>
</textClass>
</profileDesc>
</teiHeader>
<front>
<div type="abstract" xml:lang="en">The problem of out-of-vocabulary words, more precisely proper names retrieval for in speech recognition is investigated. Speech recognition vocabulary is extended using diachronic documents. This article explores a new method based on neural network (NN), proposed recently by Mikolov. The NN uses high-quality continuous representation of words from large amounts of unstructured text data and predicts surrounding words of one input word. Different strategies of using the NN to take into account lexical context are proposed. Experimental results on broadcast speech recognition and comparison with previously proposed methods show an ability of NN representation to model semantic and lexical context of proper names.</div>
</front>
</TEI>
<affiliations>
<list>
<country>
<li>France</li>
</country>
<region>
<li>Grand Est</li>
<li>Lorraine (région)</li>
</region>
<settlement>
<li>Metz</li>
<li>Nancy</li>
</settlement>
<orgName>
<li>Université de Lorraine</li>
</orgName>
</list>
<tree>
<country name="France">
<region name="Grand Est">
<name sortKey="Fohr, Dominique" sort="Fohr, Dominique" uniqKey="Fohr D" first="Dominique" last="Fohr">Dominique Fohr</name>
</region>
<name sortKey="Illina, Irina" sort="Illina, Irina" uniqKey="Illina I" first="Irina" last="Illina">Irina Illina</name>
</country>
</tree>
</affiliations>
</record>

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