Beyond the SIP Trunk: Reducing Voice Agent Latency to Sub-300ms

By IdentityCall AI Team | Engineering | 8 min read

The New Latency Standard

In 2024, the "Turing Test" for voice agents isn't just about intelligence—it's about speed. The previous standard of 800ms-1s latency (typical of cloud-based LLM chains) is no longer acceptable for high-stakes customer interactions. Users perceive delays >500ms as "robotic" or "broken," leading to talk-over and frustration.

The new gold standard is sub-300ms turn-taking. This article explores the architectural shifts required to achieve it.

The Bottleneck: Traditional Cloud Chains

The traditional voice AI stack looks like this:

1. VAD (Voice Activity Detection) waits for silence (200-500ms).
2. Audio Upload to cloud (100ms).
3. ASR (Transcription) processes full utterance (200-400ms).
4. LLM Inference generates token stream (200-800ms).
5. TTS (Synthesis) generates audio from text (200-400ms).
6. Audio Download & Playback (100ms).

Total Latency: 1.5s - 2.5s. This is why your first-gen voice bot felt slow.

The Solution: Streaming & Edge Architectures

1. Speculative Execution & Streaming ASR

Don't wait for silence. Modern architectures use streaming ASR that sends partial transcripts to the LLM while the user is still speaking.

• Technique: The LLM begins predicting the response based on the first 80% of the sentence.
• Safety: A "commitment gate" ensures the bot doesn't speak until the intent is clear, but the tokens are pre-generated.

2. Edge AI & Local VAD

Moving Voice Activity Detection (VAD) to the edge (or the telephony provider's localized node) saves critical round-trip time.

• Impact: Cuts 100-200ms of network jitter.
• Implementation: Using WebAssembly (Wasm) VAD modules running directly in the browser or telephony edge.

3. TTFT (Time to First Token) Optimization

For the LLM itself, we optimize for Time to First Token.

• Quantization: Using 4-bit quantized models (e.g., Llama-3-8B-Int4) drastically increases inference speed with negligible accuracy loss for conversational tasks.
• Cache: Semantic caching stores responses to common greetings ("Hello", "Who is this?") to serve them instantly (0ms inference).

Architecture Diagram (Mermaid)

sequenceDiagram
    participant User
    participant Edge_VAD
    participant Cloud_LLM
    participant TTS_Engine
    
    User->>Edge_VAD: Speaks "I need to reset..."
    Edge_VAD->>Cloud_LLM: Stream: "I need to reset..."
    Cloud_LLM->>Cloud_LLM: Pre-fetch "Reset Password flow"
    User->>Edge_VAD: "...my password"
    Edge_VAD->>Cloud_LLM: Stream: "...my password" [EOS]
    Cloud_LLM->>TTS_Engine: Stream Tokens (Immediate)
    TTS_Engine->>User: Audio Stream (Sub-300ms)

Conclusion

Sub-300ms latency turns a "voice bot" into a "voice agent." By moving away from rigid request-response cycles to fluid, streaming architectures, we create experiences that feel remarkably human.

Ready to build faster agents? Explore our API Documentation for streaming endpoints.