Comparison of Classification Performance Between Adult and Elderly Using Acoustic and Linguistic Features from Spontaneous Speech


KIPS Transactions on Software and Data Engineering, Vol. 12, No. 8, pp. 365-370, Aug. 2023
https://doi.org/10.3745/KTSDE.2023.12.8.365,   PDF Download:
Keywords: Spontaneous Speech, Acoustic Features, Linguistic Features, Speaker Age Recognition
Abstract

This paper aims to compare the performance of speech data classification into two groups, adult and elderly, based on the acoustic and linguistic characteristics that change due to aging, such as changes in respiratory patterns, phonation, pitch, frequency, and language expression ability. For acoustic features we used attributes related to the frequency, amplitude, and spectrum of speech voices. As for linguistic features, we extracted hidden state vector representations containing contextual information from the transcription of speech utterances using KoBERT, a Korean pre-trained language model that has shown excellent performance in natural language processing tasks. The classification performance of each model trained based on acoustic and linguistic features was evaluated, and the F1 scores of each model for the two classes, adult and elderly, were examined after address the class imbalance problem by down-sampling. The experimental results showed that using linguistic features provided better performance for classifying adult and elderly than using acoustic features, and even when the class proportions were equal, the classification performance for adult was higher than that for elderly.


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Cite this article
[IEEE Style]
S. Han, B. O. Kang, S. Dong, "Comparison of Classification Performance Between Adult and Elderly Using Acoustic and Linguistic Features from Spontaneous Speech," KIPS Transactions on Software and Data Engineering, vol. 12, no. 8, pp. 365-370, 2023. DOI: https://doi.org/10.3745/KTSDE.2023.12.8.365.

[ACM Style]
SeungHoon Han, Byung Ok Kang, and Sunghee Dong. 2023. Comparison of Classification Performance Between Adult and Elderly Using Acoustic and Linguistic Features from Spontaneous Speech. KIPS Transactions on Software and Data Engineering, 12, 8, (2023), 365-370. DOI: https://doi.org/10.3745/KTSDE.2023.12.8.365.