Toward Improved HMM-Based Speech Synthesis Using High-Level Syntactical Features

Nicolas Obin, Pierre Lanchantin, Mathieu Avanzi, Anne Lacheret-Dujour, Xavier Rodet, IRCAM

A major drawback of current HMM-based speech synthesis is the monotony of the generated speech which is closely related to the monotony of the generated prosody. Complementray 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.