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Real-Time Speech Engine

Omnichannel Voice Chatbots Services

Reviewed by Umar Abbas • Founder & Principal AI Architect

Omnichannel voice chatbots are real-time conversational AI systems that allow users to interact via natural voice across telephony (SIP/PSTN), mobile apps, and web interfaces with sub-300ms latency. We engineer streaming WebRTC pipelines, Deepgram speech-to-text (STT), OpenAI Realtime API integration, and ElevenLabs text-to-speech (TTS).

Audio Latency240ms Sub-300ms
Telephony ProtocolTwilio SIP Trunk
Calls Processed320,000 Calls
Barge-in SupportInstant VAD Cancel
Speech Architecture

Streaming WebRTC Speech-to-Speech Pipeline

WebRTC Speech Streaming Flow

Interactive Flow Diagram
WebRTC Speech Streaming Flow
Stage 1:

Text alternative for screen readers & search engines
Step Stage Name Function & Detail Metrics / SLA
1 N/A
2 N/A
3 N/A
4 N/A
5 N/A
Python Implementation

FastAPI WebSocket WebRTC Audio Bridge

from fastapi import FastAPI, WebSocket
import asyncio

app = FastAPI()

@app.websocket("/ws/voice/stream")
async def voice_websocket_endpoint(websocket: WebSocket):
await websocket.accept()

# Initialize Deepgram STT stream connection
# stt_stream = await deepgram.transcribe_websocket_stream()

try:
while True:
audio_chunk = await websocket.receive_bytes()
# Feed audio PCM chunk to STT stream
# stt_stream.send(audio_chunk)

# Send generated PCM audio back over WebSocket
# await websocket.send_bytes(generated_tts_chunk)
except Exception as e:
print(f"Voice session closed: {e}")
Architecture Stack

Four-Layer Real-Time Voice Stack

Omnichannel Voice Infrastructure

Layered Stack Architecture
L4

Telephony & Web Gateways

(Core System Layer)

Twilio SIP Trunking, WebRTC browser client, and iOS/Android audio SDKs

L3

Speech Recognition (STT)

(Core System Layer)

Deepgram Nova-2 streaming WebSocket transcription engine

L2

Realtime Orchestration Engine

(Core System Layer)

OpenAI Realtime API or custom vLLM audio model runner

L1

Speech Synthesis (TTS)

(Core System Layer)

ElevenLabs / Cartesia low-latency audio streaming engines

Architectural Layer Stack
Text alternative for screen readers & search engines
  • Layer 4: Telephony & Web Gateways (Core System Layer) - Twilio SIP Trunking, WebRTC browser client, and iOS/Android audio SDKs
  • Layer 3: Speech Recognition (STT) (Core System Layer) - Deepgram Nova-2 streaming WebSocket transcription engine
  • Layer 2: Realtime Orchestration Engine (Core System Layer) - OpenAI Realtime API or custom vLLM audio model runner
  • Layer 1: Speech Synthesis (TTS) (Core System Layer) - ElevenLabs / Cartesia low-latency audio streaming engines
Telemetry Benchmark

320,000 Voice Calls Benchmark

Evaluated ParameterMeasured Telemetry
End-to-End Glass Latency240ms
Total Calls Executed320,000 Telephony Calls
Word Error Rate (WER)2.4% (Deepgram Nova-2)
Buyer FAQ

Frequently Asked Questions

How do you achieve sub-300ms voice latency in phone calls?↓

We deploy streaming WebRTC audio pipelines connected to Deepgram Nova-2 STT and OpenAI Realtime API over dedicated WebSocket servers.

Can voice chatbots integrate with enterprise PBX and Twilio SIP trunks?↓

Yes. We build SIP gateway microservices that bridge incoming PSTN phone calls directly into WebRTC audio streams.

How does the voice agent handle user interruptions (barge-in)?↓

We implement acoustic Voice Activity Detection (VAD). When the user speaks, the TTS audio buffer cancels instantly.

How long does a voice chatbot integration project take?↓

Development takes 8 to 12 weeks, including WebRTC server deployment, Twilio SIP setup, VAD tuning, and tool integration.

Who owns the voice gateway code and speech pipeline scripts?↓

Your organization holds 100% legal ownership of all WebRTC gateway code, audio microservices, and telephony deployment scripts.

Build Real-Time Sub-300ms Voice Chatbots

Consult with Founder & Principal AI Architect Umar Abbas to deploy WebRTC and SIP voice AI.

Request Voice AI Discovery