Fast Speech Foundation Model Distillation Using Interleaved Stacking
Researchers have developed a new method called interleaved stacking to accelerate the training of speech foundation models (SFMs). This technique aims to distill large SFMs into more efficient student models, reducing inference latency without the performance degradation seen in previous stacking methods. The interleaved stacking approach preserves layer position throughout the process, which is crucial for SFMs where each layer holds specific knowledge. The effectiveness of this method was validated on the SUPERB benchmark. AI
IMPACT Accelerates the deployment of efficient speech foundation models for low-resource environments.