Revolutionizing Speech Recognition for Dysarthria: LLM-Powered Accuracy Boost!
Analysis
This research introduces a groundbreaking approach to improve Automatic Speech Recognition (ASR) for individuals with dysarthria, moving beyond the traditional word error rate (WER). By employing a Large Language Model (LLM) based Agent, the system achieves remarkable semantic gains, showcasing the potential for significantly enhanced communication for those affected by speech impairments.
Key Takeaways
- •An LLM-based Agent is used for post-ASR correction, improving semantic accuracy.
- •The research introduces SAP-Hypo5, the largest benchmark for dysarthric speech correction.
- •The system shows a significant WER reduction and semantic improvements on challenging samples.
Reference / Citation
View Original"Under multi-perspective evaluation, our agent achieves a 14.51% WER reduction alongside substantial semantic gains, including a +7.59 pp improvement in MENLI and +7.66 pp in Slot Micro F1 on challenging samples."
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ArXiv Audio SpeechJan 30, 2026 05:00
* Cited for critical analysis under Article 32.