View in EDS

Prediction of Speech Impairment in Patients Treated for Oral or Oropharyngeal Cancer Using Automatic Speech Analysis

Saved in:
Bibliographic Details
Title: Prediction of Speech Impairment in Patients Treated for Oral or Oropharyngeal Cancer Using Automatic Speech Analysis
Language: English
Authors: Mathieu Balaguer (ORCID 0000-0003-1311-4501), Julien Pinquier (ORCID 0000-0003-1556-1284), Jérôme Farinas (ORCID 0000-0002-7456-9019), Virginie Woisard (ORCID 0000-0003-3895-2827)
Source: International Journal of Language & Communication Disorders. 2025 60(5).
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: 15
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Descriptors: Prediction, Speech Impairments, Patients, Cancer, Human Body, Articulation Impairments, Artificial Intelligence, Speech Therapy, Speech Language Pathology, Reliability, Acoustics
DOI: 10.1111/1460-6984.70103
ISSN: 1368-2822
1460-6984
Abstract: Background: Perceptual evaluation of speech disorders produces scores that poorly predict the consequences of speech impairment on the communication abilities of patients treated for oral/oropharyngeal cancer. This may be mitigated by automatic speech analysis. Aim: To measure communication and speech impairment using automatic analyses of spontaneous speech and self-administered questionnaires in patients treated for oral cavity or oropharyngeal cancer. Methods and Procedures: The spontaneous speech of 25 patients was recorded during a semistructured interview. Various acoustic and automatic tools were applied to the speech signal to obtain scores relating to the different linguistic levels. Reduction of dimensionality was applied to retain only relevant and nonredundant parameters. Self-administered questionnaires assessing communication and associated factors (associated deficits, anxiety/depression, cognitive status, communication needs relating to social circles, self-perception of speech impairment and quality of life) were conducted. A predictive modelling of communication and speech impairment by LASSO regression was performed using the scores from the automatic tools alone, which were then combined with the scores arising from the questionnaires. Outcomes and Results: A total of 149 automatic parameters were extracted from the speech signal, of which 75 were retained after dimensional reduction. Predictive modelling of communication and speech impairment [Holistic Communication Score (HoCoS)] using the selected automatic parameters (number of sonants and occlusives recognised per second) provides a correlation of 0.83 between the predicted and actual score. This modelling is reliable (r[subscript s] = 0.82 between five-fold cross-validation and HoCoS). The correlation reaches 0.89 when including associated factors in the modelling, while maintaining a high reliability (r[subscript s] = 0.70 between five-fold cross-validation and HoCoS). Conclusions and Implications: The use of automatic speech analysis allows a reliable prediction of the communication and speech impairment experienced by the patients. This study opens up new perspectives for the use of automatic speech recognition systems in clinical evaluation and for the consideration of functional and psychosocial needs expressed by the patients during their follow-up.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1484170
Database: ERIC
FullText Text:
  Availability: 0
Header DbId: eric
DbLabel: ERIC
An: EJ1484170
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Prediction of Speech Impairment in Patients Treated for Oral or Oropharyngeal Cancer Using Automatic Speech Analysis
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <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="%22Julien+Pinquier%22">Julien Pinquier</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-1556-1284">0000-0003-1556-1284</externalLink>)<br /><searchLink fieldCode="AR" term="%22Jérôme+Farinas%22">Jérôme Farinas</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-7456-9019">0000-0002-7456-9019</externalLink>)<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>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22International+Journal+of+Language+%26+Communication+Disorders%22"><i>International Journal of Language & Communication Disorders</i></searchLink>. 2025 60(5).
– Name: Avail
  Label: Availability
  Group: Avail
  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
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 15
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Prediction%22">Prediction</searchLink><br /><searchLink fieldCode="DE" term="%22Speech+Impairments%22">Speech Impairments</searchLink><br /><searchLink fieldCode="DE" term="%22Patients%22">Patients</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="%22Articulation+Impairments%22">Articulation Impairments</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Speech+Therapy%22">Speech Therapy</searchLink><br /><searchLink fieldCode="DE" term="%22Speech+Language+Pathology%22">Speech Language Pathology</searchLink><br /><searchLink fieldCode="DE" term="%22Reliability%22">Reliability</searchLink><br /><searchLink fieldCode="DE" term="%22Acoustics%22">Acoustics</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1111/1460-6984.70103
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1368-2822<br />1460-6984
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Background: Perceptual evaluation of speech disorders produces scores that poorly predict the consequences of speech impairment on the communication abilities of patients treated for oral/oropharyngeal cancer. This may be mitigated by automatic speech analysis. Aim: To measure communication and speech impairment using automatic analyses of spontaneous speech and self-administered questionnaires in patients treated for oral cavity or oropharyngeal cancer. Methods and Procedures: The spontaneous speech of 25 patients was recorded during a semistructured interview. Various acoustic and automatic tools were applied to the speech signal to obtain scores relating to the different linguistic levels. Reduction of dimensionality was applied to retain only relevant and nonredundant parameters. Self-administered questionnaires assessing communication and associated factors (associated deficits, anxiety/depression, cognitive status, communication needs relating to social circles, self-perception of speech impairment and quality of life) were conducted. A predictive modelling of communication and speech impairment by LASSO regression was performed using the scores from the automatic tools alone, which were then combined with the scores arising from the questionnaires. Outcomes and Results: A total of 149 automatic parameters were extracted from the speech signal, of which 75 were retained after dimensional reduction. Predictive modelling of communication and speech impairment [Holistic Communication Score (HoCoS)] using the selected automatic parameters (number of sonants and occlusives recognised per second) provides a correlation of 0.83 between the predicted and actual score. This modelling is reliable (r[subscript s] = 0.82 between five-fold cross-validation and HoCoS). The correlation reaches 0.89 when including associated factors in the modelling, while maintaining a high reliability (r[subscript s] = 0.70 between five-fold cross-validation and HoCoS). Conclusions and Implications: The use of automatic speech analysis allows a reliable prediction of the communication and speech impairment experienced by the patients. This study opens up new perspectives for the use of automatic speech recognition systems in clinical evaluation and for the consideration of functional and psychosocial needs expressed by the patients during their follow-up.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2025
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1484170
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1484170
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/1460-6984.70103
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 15
    Subjects:
      – SubjectFull: Prediction
        Type: general
      – SubjectFull: Speech Impairments
        Type: general
      – SubjectFull: Patients
        Type: general
      – SubjectFull: Cancer
        Type: general
      – SubjectFull: Human Body
        Type: general
      – SubjectFull: Articulation Impairments
        Type: general
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Speech Therapy
        Type: general
      – SubjectFull: Speech Language Pathology
        Type: general
      – SubjectFull: Reliability
        Type: general
      – SubjectFull: Acoustics
        Type: general
    Titles:
      – TitleFull: Prediction of Speech Impairment in Patients Treated for Oral or Oropharyngeal Cancer Using Automatic Speech Analysis
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Mathieu Balaguer
      – PersonEntity:
          Name:
            NameFull: Julien Pinquier
      – PersonEntity:
          Name:
            NameFull: Jérôme Farinas
      – PersonEntity:
          Name:
            NameFull: Virginie Woisard
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 09
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 1368-2822
            – Type: issn-electronic
              Value: 1460-6984
          Numbering:
            – Type: volume
              Value: 60
            – Type: issue
              Value: 5
          Titles:
            – TitleFull: International Journal of Language & Communication Disorders
              Type: main
ResultId 1