HMM-based Prosodic Structure Model Using Rich Linguistic Context

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

1 STMS - Sciences et Technologies de la Musique et du Son
2 MoDyCo - Modèles, Dynamiques, Corpus

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).

Mots clés: Speech Prosody; Prosodic Structure; Speech Synthesis; High-Level Syntactic Analysis

Domaines: Applications [stat.AP]; Linguistique

Nicolas Obin, Xavier Rodet, Anne Lacheret. HMM-based Prosodic Structure Model Using Rich Linguistic Context. Interspeech, Sep 2010, Makuhari, Japan. pp.1133-1136. ⟨hal-00589069⟩