| 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 |