CNN-based automatic classification of stuttering: Detection of repetitions and prolongations in stuttered speech
Year: 2024
Type: article
Abstract: This study aims to develop and validate a CNN-based deep learning algorithm to automatically classify repetition and prolongation disfluency types in stuttered speech. The LibriStutter dataset was used, and the speech data were pre-processed into mel-frequency cepstral coefficients (MFCCs) to train a convolutional neural network (CNN) model. With optimized hyperparameters using the GRID search method, the model achieved high performance, with an accuracy of 0.9912 and a loss of 0.0544. Among the fluent speech and four disfluency types (sound repetitions, word repetitions, phrase repetitions, and prolongations), the model demonstrated strong classification performance for sound repetitions and prolongations, while the classification accuracy for word and phrase repetitions was comparatively lower, indicating areas for future improvement. This study demonstrates the feasibility of automated stuttering disfluency assessment and suggests further research to enhance clinical applicability by incorporating diverse datasets and multi-modal approaches.
Source: Phonetics and Speech Sciences
Authors: Jin Park, Chang Gyun Lee
Institution: Catholic Kwandong University
Language: English
Subfield: Clinical Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4406478801