음성음운형태론연구 29집 3호 Hong, Soonhyun

2024.01.31 09:27

홍보이사_2 조회 수:118

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