Speech to text becomes more reliable across different noisy settings
NOPE-HYPE: A Structured Simulation Workflow for Robust Speech-to-Text Across Diverse Acoustic Environments
SoundArtificial IntelligenceComputation and LanguageMachine Learning
Summary
Speech-to-text systems often struggle to understand speech in different noisy places because they haven’t trained on every kind of background noise. The authors created a new way to simulate various acoustic environments so that these systems can be trained more effectively. Their method uses a controlled noise simulator and tests a few settings to find good defaults. They showed that training with simulated noise performs as well as training with real noisy recordings on popular speech models. This makes speech recognition more robust in many different real-world noises.
What this means in practice
- •For speech recognition developers: Improve training of speech-to-text models by using simulated noise environments instead of relying on large noisy audio datasets.
- •For voice assistant engineers: Design voice assistants that work reliably in diverse, noisy real-world environments by adopting simulator-based environment tuning methods.
Authors
Niramay M. Patel, Bibek Behera, Raksha Sharma
Abstract
Robust speech-to-text translation systems should perform reliably across diverse acoustic conditions, yet practical pipelines lack controllable tools for systematic environment exploration. Large speech models remain sensitive to unseen acoustic conditions, as training data rarely cover the full range of real environments.We present NOPEHYPE, a structured training workflow that combines a controllable environment simulator, coverage-optimal environment reduction on Power Spectral Density (PSD) templates, and a small, interpretable hyperparameter search over simulator knobs. We show that simulator-generated noise achieves performance comparable to balanced realnoise training across Whisper and SeamlessM4T models, provide principled environment prototype sets, and identify practical default simulator configurations from a structured 27-run hyperparameter sweep.