HMM Training Strategy for Incremental Speech Synthesis - Archive ouverte HAL Access content directly
Conference Papers Year : 2015

HMM Training Strategy for Incremental Speech Synthesis

Abstract

Incremental speech synthesis aims at delivering the synthetic voice while the sentence is still being typed. One of the main challenges is the online estimation of the target prosody from a partial knowledge of the sentence's syntactic structure. In the context of HMM-based speech synthesis, this typically results in missing segmental and suprasegmental features, which describe the linguistic context of each phoneme. This study describes a voice training procedure which integrates explicitly a potential uncertainty on some contextual features. The proposed technique is compared to a baseline approach (previously published), which consists in substituting a missing contextual feature by a default value calculated on the training set. Both techniques were implemented in a HMM-based Text-To-Speech system for French, and compared using objective and perceptual measurements. Experimental results show that the proposed strategy outperforms the baseline technique for this language.
Fichier principal
Vignette du fichier
i15_1201.pdf (281.41 Ko) Télécharger le fichier
Origin : Publisher files allowed on an open archive
Loading...

Dates and versions

hal-01228889 , version 1 (14-11-2015)

Identifiers

  • HAL Id : hal-01228889 , version 1

Cite

Maël Pouget, Thomas Hueber, Gérard Bailly, Timo Baumann. HMM Training Strategy for Incremental Speech Synthesis. Interspeech 2015 - 16th Annual Conference of the International Speech Communication Association, ISCA, Sep 2015, Dresden, Germany. pp.1201-1205. ⟨hal-01228889⟩
321 View
187 Download

Share

Gmail Facebook X LinkedIn More