What happened: Voice models moved from a three-step pipeline — speech-to-text, then an LLM, then text-to-speech — to unified speech-to-speech models that respond in under 300ms, close to natural conversation pace.
Why it matters: Every hop in the old pipeline added latency and threw away information — tone, pace, emotion, interruptions all got flattened into plain text before the model ever saw them. A unified model keeps that signal.
Old pipeline vs. new
- Old: audio → transcript → text response → synthesized audio — three separate models, three failure points, noticeable lag
- New: one model reasons directly over audio in and audio out — lower latency, preserves tone and can be interrupted naturally
Where it's used
Support lines that need to feel less robotic, accessibility tools, in-car assistants, and any interface where typing isn't practical. The bar isn't "understands words" anymore — it's "doesn't feel like talking to a phone tree."
Where it still stumbles
Heavy accents and background noise remain harder than clean studio audio. It's also harder to log, audit, and debug than a text-based system — there's no transcript by default, which matters for compliance-sensitive use cases.
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