Automatic detection of articulations disorders from children’s speech preliminary study

Ramou, N.; Guerti, Mhania

doi:10.1134/s1064226914110187

Cited by 4 publications

(2 citation statements)

References 7 publications

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“… 80 , 120 , 133 Most of the proposed models focused on detecting articulation, and much work is still needed for novel models to predict the correct word. 140 More effective models are required while dealing with the extreme variability of speech due to its complex nature. 26 These models need to be able to categorize types and severity of speech disorders such as dysarthric or aphasic speech into multiple categories instead of binary classification.…”

Section: Gaps and Future Directionsmentioning

confidence: 99%

“… 26 These models need to be able to categorize types and severity of speech disorders such as dysarthric or aphasic speech into multiple categories instead of binary classification. 80 , 128 , 135 , 139 , 140 However, there are various metrics for evaluating the proposed models’ performance commonly including accuracy in, 11 , 12 , 15 , 58 , 63 , 72 , 87 , 90 , 98 , 111 , 128 F1-score in, 14 , 111 Confidence RMSE in, 87 , 131 Sensitivity, and Specificity in. 70 These metrics may not entirely capture the performance of machine learning models in real-world circumstances.…”

Section: Gaps and Future Directionsmentioning

confidence: 99%

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Exploring the Role of Machine Learning in Diagnosing and Treating Speech Disorders: A Systematic Literature Review

Brahmi,

Mahyoob,

Al-Sarem

et al. 2024

PRBM

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Purpose Speech disorders profoundly impact the overall quality of life by impeding social operations and hindering effective communication. This study addresses the gap in systematic reviews concerning machine learning-based assistive technology for individuals with speech disorders. The overarching purpose is to offer a comprehensive overview of the field through a Systematic Literature Review (SLR) and provide valuable insights into the landscape of ML-based solutions and related studies. Methods The research employs a systematic approach, utilizing a Systematic Literature Review (SLR) methodology. The study extensively examines the existing literature on machine learning-based assistive technology for speech disorders. Specific attention is given to ML techniques, characteristics of exploited datasets in the training phase, speaker languages, feature extraction techniques, and the features employed by ML algorithms. Originality This study contributes to the existing literature by systematically exploring the machine learning landscape in assistive technology for speech disorders. The originality lies in the focused investigation of ML-speech recognition for impaired speech disorder users over ten years (2014–2023). The emphasis on systematic research questions related to ML techniques, dataset characteristics, languages, feature extraction techniques, and feature sets adds a unique and comprehensive perspective to the current discourse. Findings The systematic literature review identifies significant trends and critical studies published between 2014 and 2023. In the analysis of the 65 papers from prestigious journals, support vector machines and neural networks (CNN, DNN) were the most utilized ML technique (20%, 16.92%), with the most studied disease being Dysarthria (35/65, 54% studies). Furthermore, an upsurge in using neural network-based architectures, mainly CNN and DNN, was observed after 2018. Almost half of the included studies were published between 2021 and 2022).

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