AI-based automatic detection of repetitions and prolongations: A performance comparison between deep neural network (DNN) and convolutional neural network (CNN)
Year: 2025
Type: article
Abstract: This study compares the learning characteristics and classification performance of deep neural network (DNN) and convolutional neural network (CNN) models for automatic stuttering detection using identical acoustic features, namely, Mel-frequency Cepstral Coefficients (MFCCs). To this end, the LibriStutter synthetic speech dataset containing five speech types - fluent, sound-repetition, word-repetition, phrase-repetition, and prolongation - was used. Both models were implemented in Python using the Keras library: the DNN adopted fully connected dense layers, while the CNN utilized convolutional layers designed to capture localized temporal-spectral patterns in the MFCCs. Performance was evaluated using accuracy, precision, recall, and F1-score. The CNN classifier outperformed the DNN across all evaluation metrics, showing substantial F1-score improvements in word repetition (143%) and prolongation (81%). The CNN demonstrated lower overfitting and superior generalization due to the ability of its convolutional structure to extract temporal-spectral patterns effectively. CNNs showed structural advantages in MFCC-based stuttering detection and provided a promising foundation for developing automated fluency-assessment systems. This study contributes to understanding how neural architectures affect stuttering-recognition performance and supports the advancement of AI-assisted clinical evaluation.
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: W7124345135