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    <titleInfo>
        <title>Severity Classification and Dysarthric Speech Detection using Self-Supervised Representations</title>
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    <name type="personal">
        <namePart type="given">Sanjay</namePart>
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        <namePart type="given">Priyadharshini</namePart>
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        <namePart type="given">Vijayalakshmi</namePart>
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    <name type="personal">
        <namePart type="given">Nagarajan</namePart>
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            <title>Proceedings of the 21st International Conference on Natural Language Processing (ICON)</title>
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            <namePart type="given">Sobha</namePart>
            <namePart type="family">Lalitha Devi</namePart>
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            <namePart type="given">Karunesh</namePart>
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    <abstract>Automatic detection and classification of dysarthria severity from speech provides a non-invasive and efficient diagnostic tool, offering clinicians valuable insights to guide treatment and therapy decisions. Our study evaluated two pre-trained models—wav2vec2-BASE and distilALHuBERT, for feature extraction to build speech detection and severity-level classification systems for dysarthric speech. We conducted experiments on the TDSC dataset using two approaches: a machine learning model (support vector machine, SVM) and a deep learning model (convolutional neural network, CNN). Our findings showed that features derived from distilALHuBERT significantly outperformed those from wav2vec2-BASE in both dysarthric speech detection and severity classification tasks. Notably, the distilALHuBERT features achieved 99% accuracy in automatic detection and 95% accuracy in severity classification, surpassing the performance of wav2vec2 features.</abstract>
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        <date>2024-12</date>
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%0 Conference Proceedings
%T Severity Classification and Dysarthric Speech Detection using Self-Supervised Representations
%A B, Sanjay
%A M.k, Priyadharshini
%A P, Vijayalakshmi
%A T, Nagarajan
%Y Lalitha Devi, Sobha
%Y Arora, Karunesh
%S Proceedings of the 21st International Conference on Natural Language Processing (ICON)
%D 2024
%8 December
%I NLP Association of India (NLPAI)
%C AU-KBC Research Centre, Chennai, India
%F b-etal-2024-severity
%X Automatic detection and classification of dysarthria severity from speech provides a non-invasive and efficient diagnostic tool, offering clinicians valuable insights to guide treatment and therapy decisions. Our study evaluated two pre-trained models—wav2vec2-BASE and distilALHuBERT, for feature extraction to build speech detection and severity-level classification systems for dysarthric speech. We conducted experiments on the TDSC dataset using two approaches: a machine learning model (support vector machine, SVM) and a deep learning model (convolutional neural network, CNN). Our findings showed that features derived from distilALHuBERT significantly outperformed those from wav2vec2-BASE in both dysarthric speech detection and severity classification tasks. Notably, the distilALHuBERT features achieved 99% accuracy in automatic detection and 95% accuracy in severity classification, surpassing the performance of wav2vec2 features.
%U https://aclanthology.org/2024.icon-1.74/
%P 621-628

Markdown (Informal)

[Severity Classification and Dysarthric Speech Detection using Self-Supervised Representations](https://aclanthology.org/2024.icon-1.74/) (B et al., ICON 2024)

• Severity Classification and Dysarthric Speech Detection using Self-Supervised Representations (B et al., ICON 2024)

ACL

• Sanjay B, Priyadharshini M.k, Vijayalakshmi P, and Nagarajan T. 2024. Severity Classification and Dysarthric Speech Detection using Self-Supervised Representations. In Proceedings of the 21st International Conference on Natural Language Processing (ICON), pages 621–628, AU-KBC Research Centre, Chennai, India. NLP Association of India (NLPAI).