Learning Natural Conversational Behavior in Tandem Speech-to-Speech Models with Randomized Guidance
Tandem speech-to-speech architectures couple a responsive speech frontend with an asynchronous text backend. In KAME, a large language model (LLM) serves as the backend, supplying candidate responses as guidance to the speech frontend while the user is still speaking. Ordinary conversation recordings capture the eventu...
Manato Yaguchi, Yotaro Kubo, Hikaru Asano et al.
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