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AI Research
arXiv CS.AI·May 28·8 min read
Data-Efficient On-Policy Distillation for Automatic Speech Recognition
TL;DR
Training large speech recognition models is computationally expensive and data-hungry, limiting practical deployment. Researchers developed an on-policy distillation method that transfers knowledge from a large teacher model to a smaller student model using only relevant data samples.
✦ Why It Matters
Engineers can deploy smaller, faster speech recognition models without sacrificing accuracy by using on-policy distillation techniques.
Key Takeaways
- On-policy distillation focuses training on high-uncertainty examples, reducing total data needed for ASR.
- Student models trained via this method match teacher performance with fewer parameters and lower latency.
- Technique enables deployment of speech recognition on resource-constrained devices without retraining on full datasets.
- Measure data efficiency gains and computational savings when evaluating distillation approaches for your ASR pipeline.
Full Summary
Speech recognition systems powered by neural networks require substantial computational resources and large labeled datasets, creating barriers to deployment in resource-constrained environments. On-policy distillation is a knowledge transfer technique where a smaller student model learns from a larger teacher model, but only on data samples the student finds difficult or uncertain about—rather than all available data.
This targeted approach improves data efficiency by focusing learning on high-value examples. Researchers applied this method to automatic speech recognition (ASR) tasks, measuring performance on standard benchmarks while tracking data usage and computational cost.
Results demonstrated that the student model achieved comparable accuracy to baseline approaches while requiring significantly fewer training examples and lower computational overhead. This efficiency gain is particularly valuable for deploying ASR systems on mobile devices or in low-resource settings where training data and compute are limited.