Classification of Manners of the Prevocalic Alveolar Consonants in Machine Learning Using Dynamic Formant Transitions of Vowels in Korean Spontaneous Speech

Abstract

The present study tried to classify the prevocalic manners of alveolar consonants using the formant transitions of the vowel in a Korean spontaneous speech corpus. Random forest and neural network models were trained and tested on selections of F1, F2, and F3 samples taken at the vowel onset and target of the vowel. It was found that prevocalic manners could not be manifested properly by the samples of any one or two formants, taken singly at the vowel onset or doubly at the vowel onset and target. Rather, both models trained on all the F1, F2, and F3 measurements taken doubly at the vowel onset and target, manifested the prevocalic manners robustly, though the random forest model outperformed the neural network model. The former model was further trained on additional predictors. Vowel identity facilitated model classification substan- tially more than F0, gender, speaking rate, and vowel duration. The contribution of the latter predictors was rather marginal.