Re-Sonance is presented, a novel LLM-enhanced speech-driven AAC system designed for real-time professional speaking scenarios and validate the potential of LLM-based methods for enhancing speech-driven AAC systems, paving the way for more effective and accessible communication technologies.
Abstract
Individuals with dysarthria face significant challenges in professional speaking scenarios such as conferences, presentations, and meetings, where real-time communication is crucial. While existing Augmentative and Alternative Communication (AAC) systems provide basic support, they often fail to meet the demands of professional speaking environments due to high latency and unnatural speech patterns. This paper presents Re-Sonance, a novel LLM-enhanced speech-driven AAC system designed for real-time professional speaking scenarios. By integrating Whisper ASR, Qwen LLM, and CosyVoice TTS, Re-Sonance achieves improved speech intelligibility and naturalness while maintaining real-time performance. Both subjective and objective evaluations using a Mandarin dysarthric speech dataset demonstrate that our speech reconstruction approach significantly improved intelligibility while preserving semantic coherence for speakers with mild to moderate dysarthria. Although performance remains limited for severe dysarthria cases, our findings validate the potential of LLM-based methods for enhancing speech-driven AAC systems, paving the way for more effective and accessible communication technologies.
A single-speaker case study comparing seven LoRA-family methods on two production bases for one post-stroke Hungarian male speaker with severe dysarthria, finding that attention-projection adapters substantially improve CER on both bases.
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Dysarthric speech recognition presents significant challenges due to the diverse nature of speech impairments and limitations in available data. Although conventional audio-only automatic speech recognition (ASR) systems have advanced significantly for typical speech, they often exhibit high error rates when faced with...
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