• Phonetics and Speech Sciences
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• v.15 no.4
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• pp.71-80
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• 2023

This study primarily aimed to develop an automated stuttering identification and classification method using artificial intelligence technology. In particular, this study aimed to develop a deep learning-based identification model utilizing the convolutional neural networks (CNNs) algorithm for Korean speakers who stutter. To this aim, speech data were collected from 9 adults who stutter and 9 normally-fluent speakers. The data were automatically segmented at the phrasal level using Google Cloud speech-to-text (STT), and labels such as 'fluent', 'blockage', prolongation', and 'repetition' were assigned to them. Mel frequency cepstral coefficients (MFCCs) and the CNN-based classifier were also used for detecting and classifying each type of the stuttered disfluency. However, in the case of prolongation, five results were found and, therefore, excluded from the classifier model. Results showed that the accuracy of the CNN classifier was 0.96, and the F1-score for classification performance was as follows: 'fluent' 1.00, 'blockage' 0.67, and 'repetition' 0.74. Although the effectiveness of the automatic classification identifier was validated using CNNs to detect the stuttered disfluencies, the performance was found to be inadequate especially for the blockage and prolongation types. Consequently, the establishment of a big speech database for collecting data based on the types of stuttered disfluencies was identified as a necessary foundation for improving classification performance.

• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.254-261
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• 2011

The purpose of the present study was to investigate the characteristics of disfluency in 3~5 year-old multicultural family children(MFC). 24 children(12 MFC, 12 Korean monolingual children, KMC with the same chronological age and language age) participated in this study. The experimental tasks consisted of story retelling tasks(SRT) and picture description tasks(PDT). In all the tasks, the scores of total disfluency of the MFC were significantly higher than those of the KMC. In all the tasks, the frequency of abnormal disfluency of the MFC were significantly higher than those of the KMC and the speech rates of the MFC were significantly lower than those of the KMC. The disfluency observed in MFC indicates that language ability influences on their disfluencies and fluency support of MFC is an important factor in general language support.