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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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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (80th percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

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2 Wikipedia pages

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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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Mendeley readers

Mendeley readers

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Geographical breakdown

Country Count As %
Japan 1 1%
Poland 1 1%
Germany 1 1%
Unknown 80 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 13 16%
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 15 18%
Unknown 13 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 April 2023.
All research outputs
#4,357,371
of 23,578,918 outputs
Outputs from PLOS ONE
#65,780
of 201,811 outputs
Outputs of similar age
#32,564
of 176,394 outputs
Outputs of similar age from PLOS ONE
#1,186
of 5,130 outputs
Altmetric has tracked 23,578,918 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 201,811 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.4. This one has gotten more attention than average, scoring higher than 66% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 176,394 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 80% of its contemporaries.
We're also able to compare this research output to 5,130 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.