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Génération des prononciations de noms propres à l'aide des champs aéatoires conditionnels

Identifieur interne : 005D26 ( Hal/Corpus ); précédent : 005D25; suivant : 005D27

Génération des prononciations de noms propres à l'aide des champs aéatoires conditionnels

Auteurs : Irina Illina ; Dominique Fohr ; Denis Jouvet

Source :

RBID : Hal:hal-00753381

Descripteurs français

Abstract

We propose an approach to grapheme-to-phoneme conversion for proper names based on a probabilistic method: Conditional Random Fields (CRFs). CRFs give a long term prediction, assume a relafex state independence condition and allow a tag integration. In previous work, grapheme-to-phoneme conversion using CRF has been proposed for non proper names and different CRF features are studied. In this paper, we extend this work to proper names. Moreover, we propose an algorithm for origine detection of proper names of foreign origins. The proposed system is validated on two pronunciation dictionaries. Our approach compares favorably with the performance of the state-of-the-art Joint-Multigram Models and takes advantage of the knowledge of the origin of the proper name.

Url:

Links to Exploration step

Hal:hal-00753381

Le document en format XML

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<div type="abstract" xml:lang="en">We propose an approach to grapheme-to-phoneme conversion for proper names based on a probabilistic method: Conditional Random Fields (CRFs). CRFs give a long term prediction, assume a relafex state independence condition and allow a tag integration. In previous work, grapheme-to-phoneme conversion using CRF has been proposed for non proper names and different CRF features are studied. In this paper, we extend this work to proper names. Moreover, we propose an algorithm for origine detection of proper names of foreign origins. The proposed system is validated on two pronunciation dictionaries. Our approach compares favorably with the performance of the state-of-the-art Joint-Multigram Models and takes advantage of the knowledge of the origin of the proper name.</div>
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<abstract xml:lang="en">We propose an approach to grapheme-to-phoneme conversion for proper names based on a probabilistic method: Conditional Random Fields (CRFs). CRFs give a long term prediction, assume a relafex state independence condition and allow a tag integration. In previous work, grapheme-to-phoneme conversion using CRF has been proposed for non proper names and different CRF features are studied. In this paper, we extend this work to proper names. Moreover, we propose an algorithm for origine detection of proper names of foreign origins. The proposed system is validated on two pronunciation dictionaries. Our approach compares favorably with the performance of the state-of-the-art Joint-Multigram Models and takes advantage of the knowledge of the origin of the proper name.</abstract>
<abstract xml:lang="fr">Dans cet article, nous proposons une approche de conversion graphème-phonème pour les noms propres. L'approche repose sur une méthode probabiliste : les Champs Aléatoires Conditionnels (Conditional Random Fields, CRF). Les CRFs donnent une prévision à long terme, n'exigent pas l'indépendance des observations et permettent l'intégration de tags. Dans nos travaux antérieurs, l'approche de conversion graphème-phonème utilisant les CRFs a été proposée pour les mots communs et différents paramétrages des CRFs ont été étudiés. Dans cet article, nous étendons ce travail aux noms propres. Par ailleurs, nous proposons un algorithme pour la détection de l'origine des noms propres. Le système proposé est validé sur deux dictionnaires de prononciations. Notre approche se compare favorablement aux JMM (Joint-Multigram Model, système de l'état de l'art), et tire profit de la connaissance de la langue d'origine du nom propre.</abstract>
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