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Automatic Modelling of Perceptual Judges in the Context of Head and Neck Cancer Speech Intelligibility

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Title: Automatic Modelling of Perceptual Judges in the Context of Head and Neck Cancer Speech Intelligibility
Language: English
Authors: Sebastião Quintas (ORCID 0000-0002-8693-9638), Mathieu Balaguer (ORCID 0000-0003-1311-4501), Julie Mauclair, Virginie Woisard (ORCID 0000-0003-3895-2827), Julien Pinquier
Source: International Journal of Language & Communication Disorders. 2024 59(4):1422-1435.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 14
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Descriptors: Speech Communication, Cancer, Human Body, Intelligibility, Models, Test Reliability, Measurement Objectives, Automation, Predictor Variables, Speech Impairments, Behavioral Sciences
DOI: 10.1111/1460-6984.13004
ISSN: 1368-2822
1460-6984
Abstract: Background: Perceptual measures such as speech intelligibility are known to be biased, variant and subjective, to which an automatic approach has been seen as a more reliable alternative. On the other hand, automatic approaches tend to lack explainability, an aspect that can prevent the widespread usage of these technologies clinically. Aims: In the present work, we aim to study the relationship between four perceptual parameters and speech intelligibility by automatically modelling the behaviour of six perceptual judges, in the context of head and neck cancer. From this evaluation we want to assess the different levels of relevance of each parameter as well as the different judge profiles that arise, both perceptually and automatically. Methods and Procedures: Based on a passage reading task from the Carcinologic Speech Severity Index (C2SI) corpus, six expert listeners assessed the voice quality, resonance, prosody and phonemic distortions, as well as the speech intelligibility of patients treated for oral or oropharyngeal cancer. A statistical analysis and an ensemble of automatic systems, one per judge, were devised, where speech intelligibility is predicted as a function of the four aforementioned perceptual parameters of voice quality, resonance, prosody and phonemic distortions. Outcomes and Results: The results suggest that we can automatically predict speech intelligibility as a function of the four aforementioned perceptual parameters, achieving a high correlation of 0.775 (Spearman's [rho]). Furthermore, different judge profiles were found perceptually that were successfully modelled automatically. Conclusions and Implications: The four investigated perceptual parameters influence the global rating of speech intelligibility, showing that different judge profiles emerge. The proposed automatic approach displayed a more uniform profile across all judges, displaying a more reliable, unbiased and objective prediction. The system also adds an extra layer of interpretability, since speech intelligibility is regressed as a direct function of the individual prediction of the four perceptual parameters, an improvement over more black box approaches.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1431154
Database: ERIC
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  Data: Automatic Modelling of Perceptual Judges in the Context of Head and Neck Cancer Speech Intelligibility
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  Data: English
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  Data: <searchLink fieldCode="AR" term="%22Sebastião+Quintas%22">Sebastião Quintas</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-8693-9638">0000-0002-8693-9638</externalLink>)<br /><searchLink fieldCode="AR" term="%22Mathieu+Balaguer%22">Mathieu Balaguer</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-1311-4501">0000-0003-1311-4501</externalLink>)<br /><searchLink fieldCode="AR" term="%22Julie+Mauclair%22">Julie Mauclair</searchLink><br /><searchLink fieldCode="AR" term="%22Virginie+Woisard%22">Virginie Woisard</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-3895-2827">0000-0003-3895-2827</externalLink>)<br /><searchLink fieldCode="AR" term="%22Julien+Pinquier%22">Julien Pinquier</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22International+Journal+of+Language+%26+Communication+Disorders%22"><i>International Journal of Language & Communication Disorders</i></searchLink>. 2024 59(4):1422-1435.
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  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
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  Data: 14
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  Data: 2024
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="DE" term="%22Speech+Communication%22">Speech Communication</searchLink><br /><searchLink fieldCode="DE" term="%22Cancer%22">Cancer</searchLink><br /><searchLink fieldCode="DE" term="%22Human+Body%22">Human Body</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligibility%22">Intelligibility</searchLink><br /><searchLink fieldCode="DE" term="%22Models%22">Models</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Reliability%22">Test Reliability</searchLink><br /><searchLink fieldCode="DE" term="%22Measurement+Objectives%22">Measurement Objectives</searchLink><br /><searchLink fieldCode="DE" term="%22Automation%22">Automation</searchLink><br /><searchLink fieldCode="DE" term="%22Predictor+Variables%22">Predictor Variables</searchLink><br /><searchLink fieldCode="DE" term="%22Speech+Impairments%22">Speech Impairments</searchLink><br /><searchLink fieldCode="DE" term="%22Behavioral+Sciences%22">Behavioral Sciences</searchLink>
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  Data: 10.1111/1460-6984.13004
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  Data: 1368-2822<br />1460-6984
– Name: Abstract
  Label: Abstract
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  Data: Background: Perceptual measures such as speech intelligibility are known to be biased, variant and subjective, to which an automatic approach has been seen as a more reliable alternative. On the other hand, automatic approaches tend to lack explainability, an aspect that can prevent the widespread usage of these technologies clinically. Aims: In the present work, we aim to study the relationship between four perceptual parameters and speech intelligibility by automatically modelling the behaviour of six perceptual judges, in the context of head and neck cancer. From this evaluation we want to assess the different levels of relevance of each parameter as well as the different judge profiles that arise, both perceptually and automatically. Methods and Procedures: Based on a passage reading task from the Carcinologic Speech Severity Index (C2SI) corpus, six expert listeners assessed the voice quality, resonance, prosody and phonemic distortions, as well as the speech intelligibility of patients treated for oral or oropharyngeal cancer. A statistical analysis and an ensemble of automatic systems, one per judge, were devised, where speech intelligibility is predicted as a function of the four aforementioned perceptual parameters of voice quality, resonance, prosody and phonemic distortions. Outcomes and Results: The results suggest that we can automatically predict speech intelligibility as a function of the four aforementioned perceptual parameters, achieving a high correlation of 0.775 (Spearman's [rho]). Furthermore, different judge profiles were found perceptually that were successfully modelled automatically. Conclusions and Implications: The four investigated perceptual parameters influence the global rating of speech intelligibility, showing that different judge profiles emerge. The proposed automatic approach displayed a more uniform profile across all judges, displaying a more reliable, unbiased and objective prediction. The system also adds an extra layer of interpretability, since speech intelligibility is regressed as a direct function of the individual prediction of the four perceptual parameters, an improvement over more black box approaches.
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  Data: 2024
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        Value: 10.1111/1460-6984.13004
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      – Text: English
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        PageCount: 14
        StartPage: 1422
    Subjects:
      – SubjectFull: Speech Communication
        Type: general
      – SubjectFull: Cancer
        Type: general
      – SubjectFull: Human Body
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      – SubjectFull: Intelligibility
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      – SubjectFull: Models
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      – SubjectFull: Predictor Variables
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      – SubjectFull: Speech Impairments
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      – SubjectFull: Behavioral Sciences
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      – TitleFull: Automatic Modelling of Perceptual Judges in the Context of Head and Neck Cancer Speech Intelligibility
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