Content tagged with "turn-taking"
Taking DualTurn from an Interspeech 2026 paper into a live voice agent: a ~1.5M-parameter model trained with zero manual labels, fastest end-to-end in real-call testing against cloud and open-source turn detectors, the only one in the industry that tells a backchannel from a real interruption, and set up to keep improving for free from every call it handles.
The checklist for making a voice agent "sound human": less interrupting, more backchanneling, an even exchange, mostly targets things that aren't broken. Measured against the right human (task dialogue, not casual chat), almost everything holds up except response speed, and fixing that carelessly breaks the one thing the agent already gets right.
Speech-to-speech models know when to speak but can't reason. Cascaded LLM pipelines can reason but only react to silence. DualTurn pretrains on dual-channel human conversation to bring S2S-level turn-taking into a standard ASR-LLM-TTS stack.
Dual-channel generative pretraining for learning natural turn-taking in spoken dialogue without labeled data. A 0.5B model that outperforms models 6x its size on turn prediction.