Toward improved HMM-based speech synthesis using high-level syntactical features
Nicolas Obin, Pierre Lanchantin, Mathieu Avanzi, Anne Lacheret-Dujour, Xavier Rodet · 2010
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.