Automated Diagnosis and Assessment of Dysarthric Speech Using Relevant Prosodic Features

Kadi, Kamil Lahcene; Selouani, Sid‐Ahmed; Boudraa, Bachir; Boudraa, Malika

doi:10.1007/978-94-017-8832-8_38

Cited by 9 publications

(4 citation statements)

References 19 publications

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“…Cepstral features are less effective for classifying severity levels. This is also approved by [57] and [76].…”

Section: A Discussion Of Research Questionmentioning

confidence: 99%

“…Regarding the classification techniques, advanced algorithms such as Support Vector Machines (SVM), Discriminant analysis, and deep learning models (DNN, CNN, LSTM) were frequently seen and generally delivered a strong performance. For example, SVM was widely utilized across various studies, including [31], [58], [60], [56], [63], [54], [66], [57], [72], [74], [76], and [40].…”

Section: B Discussion Of Research Questionmentioning

confidence: 99%

See 1 more Smart Citation

The Detection of Dysarthria Severity Levels Using AI Models: A Review

Al-Ali,

Al-Maadeed,

Saleh

et al. 2024

IEEE Access

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Dysarthria, a speech disorder stemming from neurological conditions, affects communication and life quality. Precise classification and severity assessment are pivotal for therapy but are often subjective in traditional speech-language pathologist evaluations. Machine learning models offer objective assessment potential, enhancing diagnostic precision. This systematic review aims to comprehensively analyze current methodologies for classifying dysarthria based on severity levels, highlighting effective features for automatic classification and optimal AI techniques. We systematically reviewed the literature on the automatic classification of dysarthria severity levels. Sources of information will include electronic databases and grey literature. Selection criteria will be established based on relevance to the research questions. The findings of this systematic review will contribute to the current understanding of dysarthria classification, inform future research, and support the development of improved diagnostic tools. The implications of these findings could be significant in advancing patient care and improving therapeutic outcomes for individuals affected by dysarthria.

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“…Cepstral features are less effective for classifying severity levels. This is also approved by [57] and [76].…”

Section: A Discussion Of Research Questionmentioning

confidence: 99%

Section: B Discussion Of Research Questionmentioning

confidence: 99%

The Detection of Dysarthria Severity Levels Using AI Models: A Review

Al-Ali,

Al-Maadeed,

Saleh

et al. 2024

IEEE Access

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show abstract

“…After being evaluated by SVR and Linear Prediction, the speeches in Wall Street Journal 1 and UA-Speech databases were divided into four levels: very low, low, mid, and high. Kadi et al also used a set of prosodic features selected by linear discriminant analysis combined with SVM and GMM, respectively, to classify dysarthria speech of the Nemours database into four severity levels and got the best classification rate of 93% [ 176 ]. Kim et al classified pathological voice using the features of abnormal changes in prosody, phonological quality, and pronunciation at the sentence level.…”

Section: Pathological Voice Recognition For Diagnosis and Evaluationmentioning

confidence: 99%

Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review

Zhang

Qiu

et al. 2023

Computers in Biology and Medicine

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“…Dysarthria affects vocal fold vibration mainly in two respects: by changing the rate of vocal fold vibration and by altering the shape of the glottal airflow pulse generated by the vocal folds. Pitch and jitter (variability of F0 across several cycles of vibration) have been explored for the assessment of dysarthria [18][19] [47]. In addition, perceptual evaluation studies of neuro-motor diseases (including dysarthric speech due to cerebral palsy and Parkinson's disease) have indicated the deterioration in voice quality factors (such as hoarseness, breathiness) with progression of the disease severity [48] [49].…”

Section: Glottal Source In Dysarthric Speechmentioning

confidence: 99%