How Real-Time Captions Work: A Technical Deep Dive

When we set out to build Caption Glass, we had one non-negotiable requirement: captions had to appear fast enough to feel like natural conversation. After months of engineering, we've achieved industry-leading sub-200ms latency. Here's how we did it.

The Challenge: Why Latency Matters

In face-to-face conversation, even a 500ms delay feels unnatural. Traditional captioning services often have 2-3 second delays, making real-time conversation impossible. For Caption Glass to truly replace the need for interpreters in everyday situations, we needed to get that latency down to imperceptible levels.

Our Technical Stack

1. Edge-Based Speech Recognition

Instead of sending audio to the cloud, Caption Glass processes speech directly on the device using a custom neural network optimized for edge computing. This eliminates network latency and ensures privacy - your conversations never leave your device.

• Custom ASIC chip: Purpose-built for speech processing
• 8GB dedicated RAM: Ensures smooth model execution
• Optimized transformer architecture: 95% accuracy at 10x speed

2. Predictive Processing Pipeline

We don't wait for someone to finish speaking before starting to process. Our pipeline begins transcription as soon as audio is detected:

1. Voice Activity Detection (VAD): <10ms to detect speech
2. Streaming ASR: Processes audio in 20ms chunks
3. Context-aware prediction: Anticipates likely next words
4. Real-time correction: Updates text as confidence improves

3. Display Optimization

Getting text to the display quickly is just as important as fast transcription:

• 120Hz OLED displays: 8.3ms refresh rate
• Direct rendering pipeline: Bypasses traditional UI frameworks
• Predictive scrolling: Anticipates text placement

The Math Behind 200ms

Here's the breakdown of our latency budget:

Audio capture:        10ms
Voice detection:      10ms  
Feature extraction:   20ms
Neural inference:     80ms
Post-processing:      30ms
Display rendering:    30ms
Buffer/overhead:      20ms
------------------------
Total:              200ms

Accuracy vs. Speed Trade-offs

Achieving low latency while maintaining high accuracy required careful optimization:

• Progressive refinement: Initial transcription at 150ms, refined by 200ms
• Context windows: Uses previous 5 seconds for better accuracy
• Speaker adaptation: Learns individual speech patterns over time

Real-World Performance

In testing across diverse environments and speakers:

• Average latency: 180ms (20ms under target)
• 95th percentile: 195ms
• Accuracy: 94.2% word accuracy in normal conversation
• Battery life: 8 hours continuous use

Future Improvements

We're not stopping here. Our roadmap includes:

1. Multi-speaker separation: Track multiple conversations simultaneously
2. Language detection: Auto-switch between 12 languages
3. Emotion indicators: Show tone and emphasis
4. Predictive completion: Start showing likely phrases before spoken

Open Source Contributions

We believe in advancing the entire field of accessible technology. We've open-sourced several components:

• FastWhisper: Our optimized speech recognition engine
• EdgeDisplay: Low-latency rendering library for AR displays
• CaptionML: Dataset of 10M captioned conversations

Check out our GitHub repository to contribute or use these tools in your own projects.

Conclusion

Building real-time captions that feel natural required rethinking every part of the pipeline. By combining custom hardware, optimized algorithms, and careful engineering, we've created a system that makes conversation accessible without compromise.

Caption Glass ships in Early 2026. Join the waitlist to be among the first to experience truly real-time captions.