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This paper proposes Self-Listening, a method for full-duplex spoken language models that feeds realized speech back as input to improve interruption recovery and consistency with actually spoken responses.
This paper evaluates the recovery of clinically required content when patients interrupt clinical voice agents, testing various LLM configurations and finding that interruption robustness varies by context and model.