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AI Research
arXiv CS.AI·May 21·8 min read
Mega-ASR: Towards In-the-wild^2 Speech Recognition via Scaling up Real-world Acoustic Simulation
TL;DR
Speech recognition models fail in noisy, real-world conditions—they miss words or invent text when audio is distorted (acoustic robustness bottleneck). Mega-ASR combines scalable synthetic data generation with progressive training to ground models in realistic acoustic variations and compositional noise.
✦ Why It Matters
Engineers can deploy speech systems in production environments—cars, streets, offices—with confidence they'll handle noise and distortion.
Key Takeaways
- Use synthetic acoustic simulation at scale to generate diverse real-world training scenarios.
- Apply progressive training: gradually increase distortion severity to maintain acoustic grounding.
- Test robustness on combined distortions (noise + reverberation + overlap), not isolated conditions.
- Validate that improvements on noisy audio don't degrade clean-speech recognition performance.
Full Summary
Current automatic speech recognition (ASR) systems—neural networks trained to convert audio to text—perform well in controlled settings but fail in real-world acoustic conditions. The acoustic robustness bottleneck describes how models lose connection to actual sound properties under severe, compositional distortions (combinations of noise, reverberation, and speaker overlap), leading to omissions or hallucinations (fabricated words).
Mega-ASR addresses this by scaling compound-data construction: synthetically generating diverse, realistic acoustic scenarios at scale. The framework uses progressive training, gradually exposing models to harder distortions while maintaining acoustic grounding.
This unified approach combines data generation with training methodology to handle in-the-wild conditions. Results demonstrate improved robustness to severe distortions while preserving accuracy on clean audio, advancing toward practical deployment in uncontrolled environments.