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
Valente, Ana Rita
Marew, Rufael
Toyin, Hawau Olamide
Al-Ali, Hamdan
Bohnen, Anelise
Becerra, Inma
Soares, Elsa Marta
Leal, Goncalo
Aldarmaki, Hanan
Supervisor
Department
Natural Language Processing
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
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Research Projects
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Abstract
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.
Citation
A. R. Valente et al., “Clinical Annotations for Automatic Stuttering Severity Assessment,” Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, May 2025, doi: 10.21437/Interspeech.2025-1916
Source
Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Conference
26th Interspeech Conference 2025
Keywords
disfluency detection, stuttering assessment
Subjects
Source
26th Interspeech Conference 2025
Publisher
International Speech Communication Association