Realtime Voice Agents with Frontier Intelligence
Official Schedule Context
- Date/time: 2026-06-29 · 2:50pm-3:10pm
- Track/room: Voice & Realtime AI · Track 6
- Speaker(s): Bohan Li
- Session type/status: session · confirmed
Official Description
Dive into how the EliseAI voice agent harness orchestrates multiple models with jagged capability
profiles to achieve realtime latency without sacrificing intelligence. Reduces p90 effective latency
overhead of ASR, TTS, and tool calling to sub 200ms, unlocking frontier models like GPT 5.5 for
voice. ### ASR: Eager Speculative Transcription We introduce speculative transcription by pairing
local Whisper or Parakeet fine-tunes for speed with API models like Scribe, Nova, or Gemini Flash
for accuracy. A local content match classifier operates at sub 10ms latency, allowing us to
immediately trigger the downstream pipeline from the fast local transcription and dynamically
replace text with the more accurate transcription if significant differences occur. This process
runs on a eager 100ms VAD delay, securely releasing the generated response audio only after a fixed
silence threshold has passed. ### LLM: Async background tool injection To eliminate expensive tool
calling round trips, we implement system leveraging async background tool injection where the
primary model makes no direct tool calls. Instead, local fine-tuned tool-calling models continuously
observe the realtime transcription stream in the background. "Fake" tool call traces are then
injected into the primary LLM’s context, which primes it for immediate, one-shot response
generation. ### TTS: Prefix caching and infilling Many Agent responses start with the same set of
3-6 words. We can cache this audio, releasing it immediately while we infill the remaining response
audio conditioned on this prefix to preserve speech prosody. With this approach, a relatively small
cache can achieve a 90% hit rate across a wide range of voices, languages and model providers.
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