Improving End-to-End Speech Recognition for Dysarthric Speech through In-Domain Data Augmentation
arXiv:2606.19797v1 Announce Type: cross Abstract: Dysarthric speech recognition is crucial for facilitating effective communication among individuals with dysarthria. However, accurately recognizing dysarthric speech poses significant challenges due to varying severity levels and limited data availability. In this paper, we explore data augmentation techniques for dysarthric automatic speech recognition (ASR) systems by fine-tuning the End-to-End pre-trained Wav2Vec2 model, with a specific focus on severity levels. To address the challenges of data scarcity and the need for extensive data in f
The continuous advancements in AI, specifically in large language models and speech recognition technologies, are enabling more sophisticated solutions for niche applications like dysarthric speech recognition.
Improving speech recognition for dysarthric individuals can significantly enhance accessibility and communication for a demographic often underserved by current AI technologies, fostering greater inclusion.
The application of data augmentation and fine-tuning pre-trained models is making dysarthric speech recognition more accurate and practical, potentially expanding its real-world use cases.
- · Individuals with dysarthria
- · Healthcare technology providers
- · AI model developers
- · Assistive technology sector
- · Traditional speech therapy methods relying solely on human intervention
- · Companies with less sophisticated, non-AI-driven assistive speech tools
Enhanced communication tools become more accessible and effective for people with speech impediments.
Increased participation of dysarthric individuals in work and social environments due to improved communication capabilities.
Ethical considerations around data privacy and bias in AI models for vulnerable populations become more prominent as these technologies proliferate.
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Read at arXiv cs.AI