1. The Sub-200ms Full-Duplex Voice Challenge
Traditional voice response systems (IVR) and conversational voice bots rely on slow, turn-based REST polling architectures that accumulate 1,500ms+ end-to-end latency per exchange. For enterprise contact centers, healthcare triage lines, and customer service automation, delays exceeding 300ms result in unnatural pauses, overlapping speech, and poor customer satisfaction scores.
To enable fluid, natural human-like voice conversations, modern speech architectures require full-duplex WebRTC streaming transport paired with real-time Voice Activity Detection (Silero VAD), streaming Whisper v3 Speech-to-Text (STT), high-throughput LLM token generation via vLLM, and low-latency text-to-speech (TTS). Explore AIConnect's specialized Whisper & Call Center Speech AI Architecture and Custom Multi-Agent Orchestration Swarms.
2. WebRTC Transport Architecture with LiveKit
Unlike HTTP/2 WebSockets or HTTP/3 chunked responses, WebRTC provides native peer-to-peer UDP transport with built-in jitter buffers, packet loss concealment, and acoustic echo cancellation (AEC).
3. Silero VAD, Whisper v3 & vLLM Streaming Pipeline
The latency budget for each stage of the voice loop must be strictly bounded:
Latency Budget Breakdown per Component:
- Voice Activity Detection (Silero VAD v4): Identifies speech start and end boundaries in under 15ms.
- Whisper v3 Streaming STT (TensorRT-LLM): Transcribes streaming audio frames with sub-30ms first-token latency.
- vLLM Generation Engine: Generates initial response tokens using FP8 quantized Llama 3.3 in sub-40ms.
- Streaming Neural TTS (ElevenLabs / Piper / Kokoro): Synthesizes raw PCM audio buffers in sub-80ms.
4. Full-Duplex Interruption & Barge-In State Management
When a human speaker interrupts the AI agent mid-sentence ("barge-in"), the agent pipeline must immediately cancel active TTS audio playback, truncate output tokens, and reset the state context buffer without corrupting conversation memory.
5. Production Python Implementation: LiveKit Voice Agent
Below is a production Python script demonstrating a LiveKit Worker Voice Agent utilizing Silero VAD, Whisper v3 STT, and vLLM:
import asyncio
from livekit.agents import AutoSubscribe, JobContext, WorkerOptions, cli, llm
from livekit.agents.pipeline import VoicePipelineAgent
from livekit.plugins import openai, silero, whisper
async def entrypoint(ctx: JobContext):
initial_ctx = llm.ChatContext().append(
role="system",
text="You are an expert AIConnect contact center voice assistant. Provide concise, direct responses."
)
await ctx.connect(auto_subscribe=AutoSubscribe.AUDIO_ONLY)
agent = VoicePipelineAgent(
vad=silero.VAD.load(),
stt=whisper.STT(model="openai/whisper-large-v3"),
llm=openai.LLM(model="meta-llama/Llama-3.3-70B-Instruct"),
tts=openai.TTS(voice="alloy"),
chat_ctx=initial_ctx,
)
agent.start(ctx.room)
await agent.say("Hello, thank you for calling AIConnect engineering support. How can I assist you today?", allow_interruptions=True)
if __name__ == "__main__":
cli.run_app(WorkerOptions(entrypoint_fnc=entrypoint))6. Architectural Recommendations & Speech AI Services
Architecting sub-200ms full-duplex conversational voice agents requires pairing low-latency WebRTC SFU infrastructure with streaming Silero VAD, TensorRT/vLLM inference acceleration, and barge-in state management.
Looking to deploy high-concurrency WebRTC voice AI or fine-tune speech models for your enterprise contact center? Learn more on our Whisper & Call Center Service Page or consult with our speech AI architects.