Build a Real-Time, Full-Duplex Voice AI with WebRTC

This guide shows how to combine WebRTC with streaming STT/TTS and an LLM to build a real-time voice AI. We cover RTC protocols (ICE/STUN/TURN and SRTP), a latency budget to keep round-trip audio under ~300 ms, and production patterns for turn-taking/barge-in and endpointing. Use the reference architecture and tips below to ship an interruptible, low-latency WebRTC voice AI.

Also known as “RTC + AI” or “AI RTC,” this approach delivers full duplex AI conversations where users can interrupt the agent naturally. In this guide we rely on WebRTC and streaming pipelines to keep the experience real-time and reliable.

RTC+AI System Architecture Design

Architecture of real-time voice AI using WebRTC (RTC + AI / AI RTC), RTC protocol (ICE/STUN/TURN), streaming STT/TTS, VAD, barge-in

flowchart LR
    subgraph Client
        A[User Voice Input] --> B[Voice collection and pre-processing]
        B --> C[RTC module]
    end

    subgraph Server
        D[Voice activity detection VAD]
        D --> E[speech recognition ASR]
        E -->|Passed Token by Token| F[large language model LLM]
        F --> G[Generate conversational responses]
        G --> H[speech synthesis TTS]
    end

    subgraph Return
        I[RTC module]
        I --> J[Voice playback]
    end


    classDef rtc fill:#DFF3FF,stroke:#4A90E2,stroke-width:2px,color:#000
    classDef ai fill:#FFF2E6,stroke:#E2A34A,stroke-width:2px,color:#000

    C:::rtc
    D:::ai
    E:::ai
    F:::ai
    G:::ai
    H:::ai

    Client --> Server -->|Live Audio Steam| Return

What Is Full-Duplex Voice AI?

Full-duplex means the user can speak while the agent is talking. The system must detect speech onset, pause TTS instantly, and resume after endpointing. This removes push-to-talk friction and makes conversations feel natural.

RTC + AI vs. WebRTC Voice AI (Terminology)

Many teams describe this stack as RTC + AI or AI RTC. In practice, production systems rely on WebRTC for real-time media while AI services (STT/LLM/TTS) run in the cloud. We’ll use both terms, but the implementation here is a WebRTC voice AI with full duplex AI behavior.

RTC Protocols for Voice AI (ICE/STUN/TURN, SRTP)

RTC is real-time communication; WebRTC is the web standard that adds device access, jitter buffering, and echo control.

• Connectivity: ICE gathers candidates; STUN/TURN help traverse NAT.
• Security: SRTP encrypts media.
• Why not plain WebSocket audio? WebSocket can work in controlled networks, but WebRTC is safer for production due to built-ins (NAT traversal, jitter buffer, AEC/AGC).

Reference Architecture: WebRTC + STT + LLM + TTS

1. Duplex audio streams over WebRTC (media + optional DataChannel).
2. Streaming STT emits partial transcripts continuously.
3. LLM plans/responds with short, speakable chunks.
4. Low-latency TTS streams audio back; playback starts immediately.
5. Interruptibility: VAD/ASR detects user speech → pause TTS → hand control to STT/LLM → resume after endpointing.

Latency Budget for Real-Time Voice AI (<300 ms)

A practical target is to keep end-to-end round-trip ≲ 300 ms.

Tips

• Deploy regionally and pin media to the nearest POP.
• Chunk/parallelize TTS; avoid long prosody buffers.
• Use VAD and stable endpointing; drop oversized frames.

Low Latency Optimization

• Model Optimization: Use lightweight AI models (e.g., DistilBERT) to reduce computation latency.
• RTC Protocol Optimization: Adjust network parameters (e.g., MTU, jitter buffer size) to reduce transmission delay.
• Edge Computing: Deploy AI models on edge nodes near users to reduce network latency.

Gantt Chart Before RTC

gantt
    title "Processing Flow Before RTC"
    dateFormat  HH:mm:ss
    axisFormat  %S secs

    section User Input
    User Voice Input               :done, des1, 00:00:00, 00:00:04

    section Speech Processing
    Voice Activity Detection (VAD) :active, des2, 00:00:04, 00:00:05
    Automatic Speech Recognition (ASR) :active, des3, 00:00:05, 00:00:06
    Large Language Model (LLM) Analysis :active, des4, 00:00:06, 00:00:08
    Text-to-Speech (TTS)           :active, des5, 00:00:08, 00:00:10

    section Response Output
    Return Synthesized Voice       :done, des6, 00:00:10, 00:00:11

Before RTC: Each stage is serial, and the next stage cannot start until the previous one is finished. The total processing time is longer (e.g., 10 seconds).

Gantt Chart After RTC

gantt
    title "Processing Flow After RTC"
    dateFormat  HH:mm:ss
    axisFormat  %S secs

    section User Input
    User Voice Input               :done, des1, 00:00:00, 00:00:04

    section Speech Processing
    Voice Activity Detection (VAD) :active, des2, 00:00:01, 00:00:03
    Automatic Speech Recognition (ASR) :active, des3, 00:00:02, 00:00:04
    Large Language Model (LLM) Analysis :active, des4, 00:00:03, 00:00:05
    Text-to-Speech (TTS)           :active, des5, 00:00:04, 00:00:06

    section Response Output
    Return Synthesized Voice (Partial Output) :done, des6, 00:00:05, 00:00:07mermaid

After RTC: Each stage supports parallel and incremental processing (e.g., VAD, ASR, and LLM), allowing the user to receive partial synthesized voice faster. The response time is significantly reduced (e.g., 5 seconds).

Turn-Taking & Barge-In (Production Patterns)

• Fire barge-in on speech start; pause TTS immediately.
• Keep dialog state across interruptions; confirm intent if overlap is ambiguous.
• Debounce ASR events to reduce false cuts; rate-limit partials.
• Log per-turn RTT, ASR lag, TTS first-byte, and drop rate.

Use Cases and Technical Advantages of RTC+AI

1. Smart Education

RTC+AI is reshaping the way education interacts in virtual classrooms, enabling real-time speech recognition and AI-driven feedback while ensuring seamless student-teacher interactions.

Case Study: An online education platform implemented RTC+AI-powered intelligent Q&A, allowing teachers to answer student questions in real-time while generating lecture notes.

2. Virtual Assistants and Smart Customer Support

RTC+AI is widely used in virtual assistants and smart customer service applications that require real-time interactions.

Case Study: A bank deployed an AI-powered voice customer support system using RTC+AI, allowing users to access account information and transaction guidance via real-time voice interactions, significantly enhancing customer satisfaction.

3. Healthcare and Telemedicine

RTC+AI enables more efficient and intelligent doctor-patient interactions in telemedicine and health monitoring.

Case Study: A telehealth platform leveraged RTC+AI to provide voice consultations, with AI-assisted symptom analysis and real-time doctor-patient interactions.

Cost, Scaling & Security

Costs come from STT/TTS minutes, LLM tokens, concurrency, and bandwidth.
Start small, cache frequent prompts and TTS, prune transcripts, encrypt media with SRTP, and monitor per-session caps.

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FAQ

Q1: What does “RTC + AI” mean? Is it the same as a WebRTC voice AI?
Many teams say RTC + AI or AI RTC when they build a full duplex AI voice experience. In production, this is typically implemented with WebRTC for real-time media plus streaming STT/LLM/TTS services.

Q2: What is RTC and how is WebRTC used in Voice AI?
RTC enables real-time media delivery; WebRTC adds device access, jitter buffering, NAT traversal (ICE/STUN/TURN) and SRTP—ideal for production voice agents.

Q3: Do I need WebRTC or can I use WebSocket audio?
WebSocket can work in controlled environments; WebRTC is safer for production thanks to built-in jitter buffer, echo control, NAT traversal, and SRTP.

Q4: How do I keep latency under 300 ms?
Stream STT/TTS, deploy regionally, parallelize TTS, and use VAD/endpointing. Track end-to-end RTT and drop oversized frames.

Q5: How to implement barge-in and turn-taking reliably?
Detect speech with VAD, pause TTS on speech start, confirm endpoint, then resume; maintain dialog state across interruptions.

Q6: What are typical costs to run a real-time voice agent?
Driven by minutes/tokens/concurrency/bandwidth. Cache TTS, limit session length, and prune transcripts to control spend.

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