Automatic modelling of perceptual judges in the context of head and neck cancer speech intelligibility

• Published in: International journal of language & communication disorders 2024-07, Vol.59 (4), p.1422-1435
• Main Authors: Quintas, Sebastião, Balaguer, Mathieu, Mauclair, Julie, Woisard, Virginie, Pinquier, Julien
• Format: Article
• Language: English
• Subjects: Adult; Aged; Artificial Intelligence; automatic speech processing; Cancer; Computer Science; Female; Head & neck cancer; head and neck cancer; Head and Neck Neoplasms - psychology; Hearing protection; Humans; Intelligibility; Judgment; Male; Middle Aged; pathological speech; Patients; Phonemics; Prosody; speaker embeddings; Speech; Speech Intelligibility; Speech Perception; Statistical analysis; Voice Quality
• ISSN: 1368-2822; 1460-6984; 1460-6984
• DOI: 10.1111/1460-6984.13004

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 ρ). 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. WHAT THIS PAPER ADDS What is already known on this subject Speech intelligibility is a clinical measure typically used in the post‐treatment assessment of speech affecting disorders, such as head and neck cancer. Their perceptual assessment is currently the main method of evaluation; however, it is known to be quite s