11th Annual Conference of the International Speech Communication Association

Makuhari, Chiba, Japan
September 26-30. 2010

HMM-Based Prosodic Structure Model Using Rich Linguistic Context

Nicolas Obin (1), Xavier Rodet (1), Anne Lacheret (2)

(1) IRCAM, Paris, France
(2) MoDyCo, University of Paris-La Défense, France

This paper presents a study on the use of deep syntactical features to improve prosody modeling. A French linguistic processing chain based on linguistic preprocessing, morpho-syntactical labeling, and deep syntactical parsing is used in order to extract syntactical features from an input text. These features are used to define more or less high-level syntactical feature sets. Such feature sets are compared on the basis of a HMM-based prosodic structure model. High-level syntactical features are shown to significantly improve the performance of the model (up to 21% error reduction combined with 19% BIC reduction).

Full Paper

Bibliographic reference.  Obin, Nicolas / Rodet, Xavier / Lacheret, Anne (2010): "HMM-based prosodic structure model using rich linguistic context", In INTERSPEECH-2010, 1133-1136.