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.
Index Terms— HMM-based speech synthesis, Prosody, High- Level Syntactical Analysis
Cite as: Obin, N., Lanchantin, P., Avanzi, M., Lacheret-Dujour, A., Rodet, X. (2010) Toward improved HMM-based speech synthesis using high-level syntactical features. Proc. Speech Prosody 2010, paper 133
@inproceedings{obin10_speechprosody,
author={Nicolas Obin and Pierre Lanchantin and Mathieu Avanzi and Anne Lacheret-Dujour and Xavier Rodet},
title={{Toward improved HMM-based speech synthesis using high-level syntactical features}},
year=2010,
booktitle={Proc. Speech Prosody 2010},
pages={paper 133}
}