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DOI: http://dx.doi.org/10.17959/sppm.2023.29.3.451

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Predicting pre- and post-vocalic stop consonant place from the vowel in a Korean spontaneous speech corpus
Soonhyun Hong (Inha University)

Abstract

A neural network model was trained on temporal F2 frequencies sampled singly or 
doubly along the first half of vowel articulation in CVX tokens and the second half in 
XVC in a Korean spontaneous speech corpus to predict pre- and post-vocalic stop 
place, respectively. Model performance on temporal F2 frequencies sampled doubly 
at vowel onset/offset and target constituted the best cues for pre- and post-vocalic stop 
place, though prevocalic place was predicted slightly better than postvocalic place. 
Then, secondary cues were added to the dynamic F2 predictors for further training. 
The individual contributions of speaking rate, F0, gender, vowel duration, and static 
F1 and F3 frequencies sampled singly at vowel onset/offset were not as big as the 
temporal F1 and F3 frequencies sampled at vowel onset/offset and target, the latter of 
which enhanced model prediction substantially. The dynamic F1 and F3 predictors 
facilitated postvocalic place prediction more than prevocalic place prediction. Vowel 
identity  enhanced  prevocalic  place  prediction  substantially  but  did  not  enhance 
postvocalic place prediction as much. This was due to the observation that F1/F2 
transitions of all vowel categories were substantially more centralized in the F1/F2 
vowel space at vowel offset than at onset or target. Vowel quality became less 
distinctive  at  vowel  offset.  Nevertheless,  temporal  formant  samples  plus  vowel 
identity constituted the best cues for stop place. 

Keywords
coarticulation, stop place, formant transitions, vowel category, corpus, neural network
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공지 [음성음운형태론연구] 온라인 논문 투고 안내 (2023년 1월 14일 수정) Manager 2016.09.02 30499
공지 [음성음운형태론연구] 논문 투고시 유의사항 (2023년 1월 14일 수정) Manager 2013.04.27 39041
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