Détection de séquences par sélection de l'historique : application à la reconnaissance automatique de la parole
Identifieur interne : 008513 ( Main/Merge ); précédent : 008512; suivant : 008514Détection de séquences par sélection de l'historique : application à la reconnaissance automatique de la parole
Auteurs : David Langlois [France] ; Kamel Smaïli [France] ; Jean-Paul Haton [France]Source :
Descripteurs français
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Abstract
Cet article s'inscrit dans la modélisation statistique du langage pour la reconnaissance automatique de la parole. Nous présentons une méthode de détermination d'unités linguistiques de bonne qualité formées de la succession de plusieurs mots. Cette méthode utilise le principe de la Sélection par l'Historique et consiste à rechercher de fortes relations distantes entre les mots. Les nouvelles unités sont composées de mots simples de notre vocabulaire de base, qui sont fortement reliés les uns aux autres. Nous adaptons le principe des multigrammes afin de construire un modèle de langage utilisant un sous ensemble de ces nouvelles unités : les plus intéressantes en terme de modélisation statistique du langage. Le nouveau modèle bigramme utilisant ces unités améliore la perplexité du modèle de référence de 21% et accroît le taux de reconnaissance du système de reconnaissance automatique de la parole à grand vocabulaire, Sirocco, de 8,7%. Le taux d'erreur, quant à lui, est diminué de 12,7% || This paper focuses on statistical language modelling for automatic speech recognition. We present a method which aims at finding linguistic units in corpus. This method, called the Selected History Principle, consists in finding strong distant relationshi
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Hal:inria-00107575Le document en format XML
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type="university">Université Nancy 2</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Institut national polytechnique de Lorraine</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="fr"><term>automatic speech recognition</term><term>methods of combination</term><term>modèles de séquences</term><term>modélisation statistique du langage</term><term>méthodes de combinaison</term><term>phrase models</term><term>reconnaissance automatique de la parole</term><term>statistical language modelling</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="fr">Cet article s'inscrit dans la modélisation statistique du langage pour la reconnaissance automatique de la parole. Nous présentons une méthode de détermination d'unités linguistiques de bonne qualité formées de la succession de plusieurs mots. Cette méthode utilise le principe de la Sélection par l'Historique et consiste à rechercher de fortes relations distantes entre les mots. Les nouvelles unités sont composées de mots simples de notre vocabulaire de base, qui sont fortement reliés les uns aux autres. Nous adaptons le principe des multigrammes afin de construire un modèle de langage utilisant un sous ensemble de ces nouvelles unités : les plus intéressantes en terme de modélisation statistique du langage. Le nouveau modèle bigramme utilisant ces unités améliore la perplexité du modèle de référence de 21% et accroît le taux de reconnaissance du système de reconnaissance automatique de la parole à grand vocabulaire, Sirocco, de 8,7%. Le taux d'erreur, quant à lui, est diminué de 12,7% || This paper focuses on statistical language modelling for automatic speech recognition. We present a method which aims at finding linguistic units in corpus. This method, called the Selected History Principle, consists in finding strong distant relationshi</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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