Towards objective and interpretable speech disorder assessment: a comparative analysis of CNN and transformer-based models
This work addresses the need for automated and unbiased speech evaluation to improve quality of life for HNC patients, though it is incremental as it builds on existing self-supervised methods.
The study tackled the problem of subjective speech disorder assessment in Head and Neck Cancer patients by proposing a self-supervised Wav2Vec2-based model for phone classification, which outperformed a CNN-based approach and correlated with perceptual measures.
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.