• Phonetics and Speech Sciences
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• v.15 no.2
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• pp.43-51
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• 2023

In this paper, we propose an approach for dialect classification based on the speed and pause of speech utterances as well as the age and gender of the speakers. Dialect classification is one of the important techniques for speech analysis. For example, an accurate dialect classification model can potentially improve the performance of speaker or speech recognition. According to previous studies, research based on deep learning using Mel-Frequency Cepstral Coefficients (MFCC) features has been the dominant approach. We focus on the acoustic differences between regions and conduct dialect classification based on the extracted features derived from the differences. In this paper, we propose an approach of extracting underexplored additional features, namely the speed and the pauses of speech utterances along with the metadata including the age and the gender of the speakers. Experimental results show that our proposed approach results in higher accuracy, especially with the speech rate feature, compared to the method only using the MFCC features. The accuracy improved from 91.02% to 97.02% compared to the previous method that only used MFCC features, by incorporating all the proposed features in this paper.

• Phonetics and Speech Sciences
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• v.14 no.1
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• pp.37-47
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• 2022

The purpose of the this study is to observe the effects of healthy adults' age on temporal features of speech and identify which could differentiate older and young adults. We examined speech rates(i.e., overall speaking rate, articulation rate), occurrence of pause, and duration of pause per utterance by utilizing the National Institute of Korean Language's open corpus. We selected a total of 30 healthy adults (10 young, 10 middle-aged, and 10 older adults) in this study. There were significant differences among the groups in the overall speaking rate, articulation rate, total occurrence of pause, the occurrence of pause between syntactic words, total duration of pause, and duration of pause between syntactic words. The older and middle-aged adults showed slower speech rates and longer and more frequent pause than young adults. But there were no significant differences among the three groups in terms of pause within syntactic word. The overall speaking rate significantly differentiated older adults from young adults. These findings suggested that the effect of speakers' age was reflected in gradual changes in the temporal features of their speech.

• Journal of Yeungnam Medical Science
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• v.11 no.2
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• pp.323-331
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• 1994

1,050 patients who visited orthodontic dental department from 1983 to 1994, were surveyed on the yearly tendency of orthodontic patient distribution and state by means of Angle's classification. The results were as follows: 1. There was increased visiting rate of patient per year and higher visiting rate in female than in male. 2. 8-15 age group was 61.4% in total visiting patients and over 20 age group was 18.5%, under 7 age group was 8.1% 3. Class I malocclusion was 42.2%, class II div 1 was 22.5%, class II-2 was 3.9%, class III was 29.1% and cleft lip & palate was 2.0% in total visiting patient. 4. As showed the living distribution, Namgu and Susunggu's patients were 43.7% of the total patients. 5. There was increased tendency for the number of the patient to be recieved orthognathic surgery.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.8
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• pp.365-370
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• 2023

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.