What Does a Pathological Speech Assessment Model Know about Acoustic Features? A Case Study on Oral and Oropharyngeal Cancer Patients

This work investigates the interpretability of a Wav2Vec 2.0based speech intelligibility assessment model for oral and oropharyngeal cancer patients through canonical correlation analysis. By measuring the correlation between the model embeddings and eGeMAPS low-level descriptors (LLDs) as an interpretable reference, we analyze how acoustic information is encoded across the model layers. The analysis is conducted at two levels: individual LLDs layer-wise, and group-level: prosodic, spectral, and voice quality. Results show that the learned representations are most strongly correlated with spectral and prosodic features, with the first MFCC coefficient yielding the highest correlations across all layers. At the group level, spectral and prosodic groups achieve correlations of 0.77 and 0.71 respectively, while voice quality reaches 0.65. Beyond model interpretability, this work also offers practical guidance on acoustic feature selection for pathological speech assessment.

Towards objective and interpretable speech disorder assessment: a comparative analysis of CNN and transformer-based models

Head and Neck Cancers (HNC) significantly impact patients' ability to speak, affecting their quality of life. Commonly used metrics for assessing pathological speech are subjective, prompting the need for automated and unbiased evaluation methods. This study proposes a self-supervised Wav2Vec2-based model for phone classification with HNC patients, to enhance accuracy and improve the discrimination of phonetic features for subsequent interpretability purpose. The impact of pre-training datasets, model size, and fine-tuning datasets and parameters are explored. Evaluation on diverse corpora reveals the effectiveness of the Wav2Vec2 architecture, outperforming a CNN-based approach, used in previous work. Correlation with perceptual measures also affirms the model relevance for impaired speech analysis. This work paves the way for better understanding of pathological speech with interpretable approaches for clinicians, by leveraging complex self-learnt speech representations.