The Technical Detail Everyone Misses in Voice AI (And Why Your Conversations Feel Awkward)

Hey everyone! 👋

I've been deep in the trenches building voice calling agents for the past year, and I want to share something that's been a game-changer for our conversation quality—but almost nobody talks about it.

The Problem You've Definitely Experienced

You know that awkward moment when:

A voice AI cuts you off mid-sentence? 😤
Or waits forever after you finish speaking? 🐌
Or doesn't respond at all because it missed what you said? 🤷

That's not the LLM's fault. That's not even the speech recognition's fault.

It's VAD and EOU detection failing.

What Are VAD and EOU?

Voice Activity Detection (VAD) = The system detecting "Is there human speech happening right now?"

End-of-Utterance Detection (EOU) = The system detecting "Is this person DONE speaking, or just pausing?"

These seem simple, but they're brutally hard to get right because:

A 500ms pause could mean you're breathing, thinking, OR finished
Background noise can trigger false activations
Different people speak at different speeds
Phone connections add artifacts
Accents and languages have different rhythms

Why This Matters MORE Than You Think

Natural human conversation happens fast. If there's more than 300ms between when you stop speaking and when the AI responds, it feels robotic.

Your budget breakdown:

VAD/EOU detection: 50-100ms
Speech-to-Text: 100-200ms
LLM processing: 50-100ms
Text-to-Speech: 100-200ms

Total: ~300-600ms ideally

If your VAD/EOU takes 200ms, you're already in trouble before you even start processing what was said!

Real-World Solutions (What I'm Using)

Free & Open Source:

Silero VAD → Lightning fast (~1ms), great for controlled environments
WebRTC VAD → Battle-tested, super lightweight, less accurate but reliable
PyAnnote Audio → Transformer-based, higher accuracy but 50-100ms latency
Whisper VAD → Best accuracy across languages but slower (200-400ms)

Commercial APIs (Production-Grade):

Deepgram → $0.0043/min, ~50ms latency, excellent noise handling (this is what I use)
AssemblyAI → $0.015/min, great with various accents
Picovoice Cobra → On-device, sub-10ms, perfect for privacy-sensitive apps

My Production Setup (The Hybrid Approach)

I don't rely on just one solution. Here's what works:

First filter: WebRTC VAD (super fast, filters obvious silence/noise)
Confirmation: Silero VAD (catches actual speech with ML)
Backup rule: If uncertain after 1.5s, assume they're done speaking
Adaptive learning: Track when users interrupt the AI → tune timeout

This gives me <50ms VAD/EOU processing with 85-90% accuracy in real-world conditions.

Common Mistakes I See

❌ Using only timeout-based EOU (wait 1.5s of silence) → Feels super slow

❌ Not testing with background noise → Works in lab, fails in real world

❌ Same timeout for all queries → "What's the weather" needs fast response, storytelling needs patience

❌ Ignoring false positive rate → Users hate being interrupted more than waiting an extra 200ms

❌ Not monitoring in production → You need metrics on interruptions, missed speech, response delay

If You Want to Train Your Own

Sometimes off-the-shelf doesn't cut it. To build a custom VAD/EOU:

You need:

100+ hours of labeled audio (speech vs silence vs noise)
Diverse speakers, environments, accents
GPU for training (transformer models especially)

Architecture choices:

CNN: Fast inference, good accuracy, easiest to deploy
RNN/LSTM: Better temporal understanding, slower
Transformer: Best accuracy, highest latency, needs optimization

Optimization tricks:

Quantization (INT8 instead of FP32) → 4x faster
ONNX Runtime or TensorRT for inference
Process in 20-50ms chunks for streaming
Use smaller context windows if possible

The Metrics I Track

In production, I obsessively monitor:

📊 False Positive Rate: How often AI interrupts users (should be <2%)

📊 False Negative Rate: How often AI misses speech (should be <1%)

📊 Average Response Delay: Time from speech end to AI response (target <400ms)

📊 User Interruption Rate: How often users cut off the AI (indicates EOU is too slow)

📊 Conversation Completion: Do users finish or hang up frustrated?

These numbers tell you if your VAD/EOU is actually working in the real world.

Questions for the Community 🤔

What VAD/EOU are you using? Curious what's working for others
What latency are you hitting end-to-end? I'm at ~450ms average
Biggest challenge you've faced? Mine was handling Indian English accents
Ever trained a custom model? Worth it or overkill?

Key Takeaway

Most people building voice AI obsess over the LLM choice and ignore VAD/EOU. But here's the truth:

Your users don't care if you're using GPT-4 or Claude if the conversation feels awkward because of bad turn-taking.

VAD and EOU are 80% of the voice UX. Get these right first, optimize the LLM later.

Drop your questions below! I'll answer everything and share more technical details if anyone wants to dive deeper into implementation. 🚀

Also, if you're building voice AI and want to discuss specific challenges, feel free to DM me. Always happy to brainstorm with fellow builders.

P.S. I wrote a detailed technical breakdown of training pipelines, architecture choices, and deployment strategies if anyone wants the full deep-dive. Let me know!

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