Cross-lingual Dysarthria Severity Classification for English, Korean, and Tamil

Yeo, Eun Jung; Choi, Kwanghee; Kim, Sun‐Hee; Chung, Minhwa

doi:10.23919/apsipaasc55919.2022.9980124

Cited by 3 publications

(1 citation statement)

References 33 publications

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“…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%

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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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

View full text Add to dashboard Cite

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Integrating binary classification and clustering for multi-class dysarthria severity level classification: a two-stage approach

Al-Ali,

Haris,

Akbari

et al. 2024

Cluster Comput

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Dysarthria, a motor speech disorder, poses challenges in accurate severity assessment. Recent research has excelled in classifying dysarthria based on severity levels, primarily utilizing annotated datasets and achieving high accuracies. However, these classification-based approaches may not readily translate to real-world scenarios without predefined labels. This study follows a different path by proposing a two-stage approach leveraging binary classification and clustering to comprehensively analyze and classify dysarthria severity levels. We begin by employing binary classification to differentiate control from dysarthric cases by experiencing eight different feature extraction techniques and two classifiers in order to support the largest amount of dysarthric cases to the second stage, where k-means clustering uncovers hidden patterns and boundaries within dysarthria severity levels, enabling a more nuanced understanding of the disorder. We applied our methodology to the TORGO dataset, a benchmark in dysarthria research, and evaluated it on the UA Speech dataset. After optimizing the number of clusters, our approach achieved an accuracy of 91% with sentence-based features and 85% with word-based features in clustering. This research extends previous studies by exploring unsupervised clustering to differentiate severity levels in unannotated cases, bridging the gap between controlled datasets and practical applications. Our findings highlight the effectiveness of clustering-driven two-stage analysis in improving dysarthria severity-level classification, with implications for real-world clinical settings.

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Automatic cross‐ and multi‐lingual recognition of dysphonia by ensemble classification using deep speaker embedding models

Aziz,

Sztahó

2024

Expert Systems

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Machine Learning (ML) algorithms have demonstrated remarkable performance in dysphonia detection using speech samples. However, their efficacy often diminishes when tested on languages different from the training data, raising questions about their suitability in clinical settings. This study aims to develop a robust method for cross‐ and multi‐lingual dysphonia detection that overcomes the limitation of language dependency in existing ML methods. We propose an innovative approach that leverages speech embeddings from speaker verification models, especially ECAPA and x‐vector and employs a majority voting ensemble classifier. We utilize speech features extracted from ECAPA and x‐vector embeddings to train three distinct classifiers. The significant advantage of these embedding models lies in their capability to capture speaker characteristics in a language‐independent manner, forming fixed‐dimensional feature spaces. Additionally, we investigate the impact of generating synthetic data within the embedding feature space using the Synthetic Minority Oversampling Technique (SMOTE). Our experimental results unveil the effectiveness of the proposed method for dysphonia detection. Compared to results obtained from x‐vector embeddings, ECAPA consistently demonstrates superior performance in distinguishing between healthy and dysphonic speech, achieving accuracy values of 93.33% and 96.55% in both cross‐lingual and multi‐lingual scenarios, respectively. This highlights the remarkable capabilities of speaker verification models, especially ECAPA, in capturing language‐independent features that enhance overall detection performance. The proposed method effectively addresses the challenges of language dependency in dysphonia detection. ECAPA embeddings, combined with majority voting ensemble classifiers, show significant potential for improving the accuracy and reliability of dysphonia detection in cross‐ and multi‐lingual scenarios.

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Cross-lingual Dysarthria Severity Classification for English, Korean, and Tamil

Cited by 3 publications

References 33 publications

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

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

Integrating binary classification and clustering for multi-class dysarthria severity level classification: a two-stage approach

Automatic cross‐ and multi‐lingual recognition of dysphonia by ensemble classification using deep speaker embedding models

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