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.

这项研究提出了一种基于自我监督的 Wav2Vec2 模型，用于头颈癌患者的语音分类，以提高准确性和语音特征辨别能力，并通过评估多样化语料库的方式验证了该模型的有效性，为医生提供了可解释的方法来理解病理性语音。