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    %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/
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