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The Korean Association for the Study of English Language and Linguistics
[ Article ]
Korea Journal of English Language and Linguistics - Vol. 25, No. 0, pp.289-310
ISSN: 1598-1398 (Print) 2586-7474 (Online)
Print publication date 31 Jan 2025
Received 02 Jan 2025 Revised 10 Feb 2025 Accepted 08 Mar 2025
DOI: https://doi.org/10.15738/kjell.25..202503.289

Dynamic Cues in Vowel Classification: A Discriminant Analysis of Conversational Speech Corpus

Hyun Jin Hwangbo
Assistant Professor, Division of English Language and Literature Pukyong National University hjhwangbo@pknu.ac.kr


© 2025 KASELL All rights reserved
This is an open-access article distributed under the terms of the Creative Commons License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper asks whether the vowel inherent spectral change (VISC) or the dynamic cues of vowels is an essential feature for vowel classification in natural speech. To answer this question, vowels from the Buckeye Corpus of conversational speech were trained and tested for three models on vowel classification with quadratic discriminant analysis, a machine learning technique. Three models were evaluated: the steady-state model, the one-point model, and two trajectory models, which include the two-point and three-point models. The one-point model samples the spectral features of vowels at one point of vowel duration, while the two-point and three-point models sample the features at two and three points of vowel duration. Various combinations of sampled points and predictors (F0, F1, F2, and F3) were analyzed, and the combinations with the best classification accuracy were compared across the models. The results showed that the steady-state model showed the highest classification accuracy when the spectral features and fundamental frequency were sampled at 50% of vowel duration, while the trajectory models showed the highest classification when sampled at 30% and 70% and 10%, 50%, and 90% for two-point and three-point models, respectively. Classification performance was the highest for all models when all parameters (F0, F1, F2, F3) were included across all models. When compared across the models, the trajectory models perform better than the steady-state model. In addition, vowel duration as a parameter has facilitated the classification accuracy for specific vowels. This paper obtains additional evidence for VISC in vowel classification, including detailed classification results of each vowel, identifying the misclassified vowels, and providing insights for vowel classification models.

Keywords:

vowel classification, vowel dynamics, discriminant analysis, conversational speech corpus

Acknowledgments

This work was supported by a Research Grant of Pukyong National University (2023).

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