Automated Assessment of Speech Intelligibility and Severity in Patients with Head and Neck Cancers using ASR-Powered Wav2Vec2

The core message of this paper is that using a Wav2Vec2 model pre-trained on automatic speech recognition (ASR) tasks can outperform models pre-trained on self-supervised learning (SSL) tasks for the assessment of speech intelligibility and severity in patients with head and neck cancers, even with limited training data.