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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/