Speech-to-Speech Model Research at Google DeepMind
Official Schedule Context
- Date/time: 2026-06-29 · 11:10am-11:30am
- Track/room: Voice & Realtime AI · Track 6
- Speaker(s): Valeria Wu Fon, Tom Ouyang
- Session type/status: session · confirmed
Official Description
Most voice interfaces today are built as a 3-way cascade system (ASR/LLM/TTS). While functional,
this cascaded approach introduces latency bottlenecks, strips away non-verbal nuance, and limits
emotion-aware, multi-turn dialogue. Today, we are witnessing a profound shift toward native speech-
to-speech models that process audio natively from end to end. In this session, we’ll explore the
exciting paradigm at Google DeepMind to train speech-to-speech models for real-time voice agents. We
will cover the high-level product and research challenges of building voice agents that feel truly
conversational, optimizing for fluid turn-taking and low latency while maintaining enterprise-grade
intelligence.
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