A recent study in Nature Communications evaluated a newly developed wearable artificial intelligence (AI)-driven intelligent throat (IT) system that integrated throat muscle vibration and carotid pulse signal sensors with a large language model (LLM) processing to enable more continuous communication and, optionally, expanded, emotionally aligned sentences in controlled experimental settings.
The system captures laryngeal muscle vibrations and carotid pulse signals, integrating real-time analysis of silent speech and emotional state to generate either direct text output or expanded, contextually appropriate sentences that reflect patients' intended meaning during everyday-style communication tasks.
LLMs functioned as intelligent agents, automatically correcting token classification errors and generating personalized, context-aware speech by incorporating emotional states and objective contextual information such as time of day and weather, retrieved via a local software interface.
Overall, the system achieved a 4.2 % word error rate and a 2.9 % sentence error rate under optimized synthesis conditions, along with 83.2 % emotion recognition accuracy.
Patient satisfaction increased by 55 % when using the sentence expansion mode compared with direct output, suggesting that even brief, effort-efficient inputs could be transformed into fuller, socially usable expressions.
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