Clinical Annotations for Automatic Stuttering Severity Assessment
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910978314076160 |
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| author | Valente, Ana Rita Marew, Rufael Toyin, Hawau Olamide Al-Ali, Hamdan Bohnen, Anelise Becerra, Inma Soares, Elsa Marta Leal, Goncalo Aldarmaki, Hanan |
| author_facet | Valente, Ana Rita Marew, Rufael Toyin, Hawau Olamide Al-Ali, Hamdan Bohnen, Anelise Becerra, Inma Soares, Elsa Marta Leal, Goncalo Aldarmaki, Hanan |
| contents | Stuttering is a complex disorder that requires specialized expertise for effective assessment and treatment. This paper presents an effort to enhance the FluencyBank dataset with a new stuttering annotation scheme based on established clinical standards. To achieve high-quality annotations, we hired expert clinicians to label the data, ensuring that the resulting annotations mirror real-world clinical expertise. The annotations are multi-modal, incorporating audiovisual features for the detection and classification of stuttering moments, secondary behaviors, and tension scores. In addition to individual annotations, we additionally provide a test set with highly reliable annotations based on expert consensus for assessing individual annotators and machine learning models. Our experiments and analysis illustrate the complexity of this task that necessitates extensive clinical expertise for valid training and evaluation of stuttering assessment models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00644 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Clinical Annotations for Automatic Stuttering Severity Assessment Valente, Ana Rita Marew, Rufael Toyin, Hawau Olamide Al-Ali, Hamdan Bohnen, Anelise Becerra, Inma Soares, Elsa Marta Leal, Goncalo Aldarmaki, Hanan Computation and Language Artificial Intelligence Stuttering is a complex disorder that requires specialized expertise for effective assessment and treatment. This paper presents an effort to enhance the FluencyBank dataset with a new stuttering annotation scheme based on established clinical standards. To achieve high-quality annotations, we hired expert clinicians to label the data, ensuring that the resulting annotations mirror real-world clinical expertise. The annotations are multi-modal, incorporating audiovisual features for the detection and classification of stuttering moments, secondary behaviors, and tension scores. In addition to individual annotations, we additionally provide a test set with highly reliable annotations based on expert consensus for assessing individual annotators and machine learning models. Our experiments and analysis illustrate the complexity of this task that necessitates extensive clinical expertise for valid training and evaluation of stuttering assessment models. |
| title | Clinical Annotations for Automatic Stuttering Severity Assessment |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2506.00644 |