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

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article
 

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Titre

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

Nom(s)

Obin, Nicolas (auteur)
 
Lanchantin, Pierre (auteur)
 
Avanzi, Mathieu (auteur)
 
Lacheret-Dujour, Anne (auteur)
 
Rodet, Xavier (auteur)
 

Publication

Chicago, USA , 2010
 

Description

Sujet(s)

HMM-based speech synthesis   Prosody   High-Level Syntactical Analysis
 

Résumé

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.
 

Note(s)

Contribution au colloque ou congrès : Speech Prosody
 

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Date de la notice

2011-07-01 02:00:00
 

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