Wearable intelligent throat enables natural speech in stroke patients with dysarthria
Abstract
Abstract 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.
Article Details
Authors (23)
Chenyu Tang
Electrical Engineering Division, Department of Engineering, University of Cambridge
Shuo Gao
School of Instrumentation and Optoelectronic Engineering, Beihang University
Cong Li
Wentian Yi
Electrical Engineering Division, Department of Engineering, University of Cambridge
Yuxuan Jin
The Cavendish Laboratory, Department of Physics, University of Cambridge
Xiaoxue Zhai
Sixuan Lei
Hongbei Meng
Zibo Zhang
Electrical Engineering Division, Department of Engineering, University of Cambridge
Muzi Xu
Electrical Engineering Division, Department of Engineering, University of Cambridge
Shengbo Wang
Xuhang Chen
Department of Clinical Neurosciences, University of Cambridge
Chenxi Wang
Academy of Military Medical Science, Research Unit of Cell Death Mechanism, 2021RU008, Chinese Academy of Medical Science
Hongyun Yang
Ningli Wang
Wenyu Wang
Department of Pharmaceutics, School of Pharmacy
Jin Cao
Tianjin University of Technology , , ,
Xiaodong Feng
Peter Smielewski
Department of Clinical Neurosciences, University of Cambridge
Yu Pan
College of Materials Science and Engineering and Center of Quantum Materials & Devices
Wenhui Song
Martin Birchall
Luigi G. Occhipinti
Electrical Engineering Division, Department of Engineering, University of Cambridge