Ircam-Centre Pompidou

Recherche

  • Recherche simple
  • Recherche avancée

    Panier électronique

    Votre panier ne contient aucune notice

    Connexion à la base

  • Identification
    (Identifiez-vous pour accéder aux fonctions de mise à jour. Utilisez votre login-password de courrier électronique)

    Entrepôt OAI-PMH

  • Soumettre une requête

    Consulter la notice détailléeConsulter la notice détaillée
    Version complète en ligneVersion complète en ligne
    Version complète en ligne accessible uniquement depuis l'IrcamVersion complète en ligne accessible uniquement depuis l'Ircam
    Ajouter la notice au panierAjouter la notice au panier
    Retirer la notice du panierRetirer la notice du panier

  • English version
    (full translation not yet available)
  • Liste complète des articles

  • Consultation des notices


    Vue détaillée Vue Refer Vue Labintel Vue BibTeX  

    %0 Conference Proceedings
    %A Obin, Nicolas
    %A Lanchantin, Pierre
    %A Avanzi, Mathieu
    %A Lacheret-Dujour, Anne
    %A Rodet, Xavier
    %T Toward Improved HMM-based Speech Synthesis Using High-Level Syntactical Features
    %D 2010
    %B Speech Prosody
    %C Chicago
    %F Obin10a
    %K HMM-based speech synthesis
    %K Prosody
    %K High-Level Syntactical Analysis
    %X A major drawback of current Hidden Markov Model (HMM)-based speech synthesis is the monotony of the generated speech which is closely related to the monotony of the generated prosody. Complementary to model-oriented approaches that aim to increase the prosodic variability by reducing the ”over-smoothing” effect, this paper presents a linguistic-oriented approach in which high-level linguistic features are extracted from text in order to improve prosody modeling. A linguistic processing chain based on linguistic preprocessing, morpho-syntactical labeling, and syntactical parsing is used to extract high-level syntactical features from an input text. Such linguistic features are then introduced into a HMM-based speech synthesis system to model prosodic variations (f0, duration, and spectral variations). Subjective evaluation reveals that the proposed approach significantly improve speech synthesis compared to a baseline model, event if such improvement depends on the observed linguistic phenomenon.
    %1 6
    %2 1
    %U http://architexte.ircam.fr/textes/Obin10a/

    © Ircam - Centre Pompidou 2005.