Dysarthric Speech

Dysarthric speech, characterized by impaired articulation due to neurological conditions, presents a significant challenge for automatic speech recognition (ASR) and related applications. Current research focuses on developing robust ASR systems for dysarthric speech using techniques like self-supervised learning (e.g., HuBERT, wav2vec 2.0), prototype-based adaptation, and generative adversarial networks (GANs) to address data scarcity and inter-speaker variability. These advancements aim to improve speech recognition accuracy, intelligibility assessment, and even severity classification, ultimately enhancing communication and quality of life for individuals with dysarthria.