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Modeling Consonant-Vowel Coarticulation for Articulatory Speech Synthesis

Overview of attention for article published in PLOS ONE, April 2013
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Title
Modeling Consonant-Vowel Coarticulation for Articulatory Speech Synthesis
Published in
PLOS ONE, April 2013
DOI 10.1371/journal.pone.0060603
Pubmed ID
Authors

Peter Birkholz

Abstract

A central challenge for articulatory speech synthesis is the simulation of realistic articulatory movements, which is critical for the generation of highly natural and intelligible speech. This includes modeling coarticulation, i.e., the context-dependent variation of the articulatory and acoustic realization of phonemes, especially of consonants. Here we propose a method to simulate the context-sensitive articulation of consonants in consonant-vowel syllables. To achieve this, the vocal tract target shape of a consonant in the context of a given vowel is derived as the weighted average of three measured and acoustically-optimized reference vocal tract shapes for that consonant in the context of the corner vowels /a/, /i/, and /u/. The weights are determined by mapping the target shape of the given context vowel into the vowel subspace spanned by the corner vowels. The model was applied for the synthesis of consonant-vowel syllables with the consonants /b/, /d/, /g/, /l/, /r/, /m/, /n/ in all combinations with the eight long German vowels. In a perception test, the mean recognition rate for the consonants in the isolated syllables was 82.4%. This demonstrates the potential of the approach for highly intelligible articulatory speech synthesis.

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Geographical breakdown

Country Count As %
Japan 1 1%
Poland 1 1%
Germany 1 1%
Unknown 79 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 24%
Researcher 15 18%
Student > Master 11 13%
Student > Bachelor 10 12%
Professor > Associate Professor 4 5%
Other 12 15%
Unknown 10 12%
Readers by discipline Count As %
Engineering 20 24%
Computer Science 14 17%
Linguistics 10 12%
Neuroscience 6 7%
Psychology 5 6%
Other 14 17%
Unknown 13 16%