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Communication Dans Un Congrès Année : 2010

Towards Improved HMM-based Speech Synthesis Using High-Level Syntactical Features.

Résumé

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.
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Dates et versions

hal-00589083 , version 1 (27-04-2011)

Identifiants

  • HAL Id : hal-00589083 , version 1

Citer

Nicolas Obin, Pierre Lanchantin, Mathieu Avanzi, Anne Lacheret, Xavier Rodet. Towards Improved HMM-based Speech Synthesis Using High-Level Syntactical Features.. Speech Prosody, 2010, Chicago, United States. pp.2000-2004. ⟨hal-00589083⟩
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