Stuttering Disfluency Detection Using Machine Learning Approaches

Abedal-Kareem Al-Banna, Eran Edirisinghe (), Hui Fang () and Wael Hadi ()
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Abedal-Kareem Al-Banna: Department of Computer Science, Loughborough University, Epinal Way, Loughborough LE11 3TU, UK
Eran Edirisinghe: School of Computing & Mathematics, Keele University, Keele, Newcastle ST5 5BG, UK
Hui Fang: Department of Computer Science, Loughborough University, Epinal Way, Loughborough LE11 3TU, UK
Wael Hadi: Department of Information Security, University of Petra, Amman, Jordan

Journal of Information & Knowledge Management (JIKM), 2022, vol. 21, issue 02, 1-16

Abstract: Stuttering is a neurodevelopmental speech disorder wherein people suffer from disfluency in speech generation. Recent research has applied machine learning and deep learning approaches to stuttering disfluency recognition and classification. However, these studies have focussed on small datasets, generated by a limited number of speakers and within specific tasks, such as reading. This paper rigorously investigates the effective use of eight well-known machine learning classifiers, on two publicly available datasets (FluencyBank and SEP-28k) to automatically detect stuttering disfluency using multiple objective metrics, i.e. prediction accuracy, recall, precision, F1-score, and AUC measures. Our experimental results on the two datasets show that the Random Forest classifier achieves the best performance, with an accuracy of 50.3% and 50.35%, a recall of 50% and 42%, a precision of 42% and 46%, and an F1 score of 42% and 34%, against the FluencyBank and SEP-28K datasets, respectively. Moreover, we show that the machine learning-based approaches may not be effective in accurate stuttering disfluency evaluation, due to diverse variations in speech rate, and differences in vocal tracts between children and adults. We argue that the use of deep learning approaches and Automatic Speech Recognition (ASR) with language models may improve outcomes, specifically for large scale and imbalanced datasets.

Keywords: Stuttering; stammering; stuttering detection; machine learning (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0219649222500204

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