Wearable intelligent throat enables natural speech in stroke patients with dysarthria

TL;DR

This paper introduces an AI-powered wearable throat device that captures neck signals and uses large language models to enable natural, emotionally expressive speech for stroke patients with dysarthria, improving communication quality.

Contribution

The study presents a novel wearable intelligent throat system integrating sensors and LLMs for real-time, coherent speech in dysarthria patients, demonstrating significant accuracy and user satisfaction improvements.

Findings

Achieved low word error rate of 4.2% and sentence error rate of 2.9%.

Increased user satisfaction by 55%.

Enabled seamless, emotionally expressive communication.

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.