智能喉戴设备让中风失语患者自然流畅说话
Wearable intelligent throat enables natural speech in stroke patients with dysarthria
- 用颈部肌震与颈动脉脉搏信号，结合大语言模型实时解码
- 五名患者测试中词错误率4.2%，满意度提升55%
- 支持情绪表达和连续对话，适合神经疾病患者使用
可穿戴无声语音系统在恢复言语障碍患者交流能力方面具有巨大潜力，但实现流畅连贯的语音仍具挑战，临床效果尚未证实。本文提出一种基于人工智能的智能喉（IT）系统，集成颈部肌肉振动与颈动脉脉搏信号传感器，并结合大语言模型（LLM）处理，实现流利且富有情感的交流。系统采用超灵敏纺织应变传感器捕捉颈部高质量信号，支持令牌级实时连续解码，实现无延迟通信。在五名中风性构音障碍患者测试中，其LLM代理能智能纠正令牌错误，增强句级情感与逻辑连贯性，实现4.2%词错误率、2.9%句错误率，用户满意度提升55%。该研究构建了一个便携、直观的沟通平台，具备广泛应用于不同神经系统疾病及多语言支持系统的潜力。
原文摘要 · Abstract (English)
Wearable silent speech systems hold significant potential for restoring communication in patients with speech impairments. However, seamless, coherent speech remains elusive, and clinical efficacy is still unproven. Here, we present an AI-driven intelligent throat (IT) system that integrates throat muscle vibrations and carotid pulse signal sensors with large language model (LLM) processing to enable fluent, emotionally expressive communication. The system utilizes ultrasensitive textile strain sensors to capture high-quality signals from the neck area and supports token-level processing for real-time, continuous speech decoding, enabling seamless, delay-free communication. In tests with five stroke patients with dysarthria, IT's LLM agents intelligently corrected token errors and enriched sentence-level emotional and logical coherence, achieving low error rates (4.2% word error rate, 2.9% sentence error rate) and a 55% increase in user satisfaction. This work establishes a portable, intuitive communication platform for patients with dysarthria with the potential to be applied broadly across different neurological conditions and in multi-language support systems.
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