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Adding new words into a language model using parameters of known words with similar behavior

Identifieur interne : 000280 ( Main/Exploration ); précédent : 000279; suivant : 000281

Adding new words into a language model using parameters of known words with similar behavior

Auteurs : Luiza Orosanu [France] ; Denis Jouvet [France]

Source :

RBID : Hal:hal-01184194

English descriptors

Abstract

This article presents a study on how to automatically add new words into a language model without retraining it or adapting it (which requires a lot of new data). The proposed approach consists in finding a list of similar words for each new word to be added in the language model. Based on a small set of sentences containing the new words and on a set of n-gram counts containing the known words, we search for known words which have the most similar neighbor distribution (of the few preceding and few following neighbor words) to the new words. The similar words are determined through the computation of KL divergences on the distribution of neighbor words. The n-gram parameter values associated to the similar words are then used to define the n-gram parameter values of the new words. In the context of speech recognition, the performance assessment on a LVCSR task shows the benefit of the proposed approach.

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Le document en format XML

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</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>
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<profileDesc>
<textClass>
<keywords scheme="mix" xml:lang="en">
<term>OOV words</term>
<term>language modeling</term>
<term>lemmas</term>
<term>part-of-speech tags</term>
<term>similar words</term>
<term>speech-to-text transcriptions</term>
</keywords>
</textClass>
</profileDesc>
</teiHeader>
<front>
<div type="abstract" xml:lang="en">This article presents a study on how to automatically add new words into a language model without retraining it or adapting it (which requires a lot of new data). The proposed approach consists in finding a list of similar words for each new word to be added in the language model. Based on a small set of sentences containing the new words and on a set of n-gram counts containing the known words, we search for known words which have the most similar neighbor distribution (of the few preceding and few following neighbor words) to the new words. The similar words are determined through the computation of KL divergences on the distribution of neighbor words. The n-gram parameter values associated to the similar words are then used to define the n-gram parameter values of the new words. In the context of speech recognition, the performance assessment on a LVCSR task shows the benefit of the proposed approach.</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="Orosanu, Luiza" sort="Orosanu, Luiza" uniqKey="Orosanu L" first="Luiza" last="Orosanu">Luiza Orosanu</name>
</region>
<name sortKey="Jouvet, Denis" sort="Jouvet, Denis" uniqKey="Jouvet D" first="Denis" last="Jouvet">Denis Jouvet</name>
</country>
</tree>
</affiliations>
</record>

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