Fast Speech Foundation Model Distillation Using Interleaved Stacking

Researchers propose interleaved stacking, a novel training method for distilling large speech foundation models into efficient student models while accelerating training speed. The technique maintains consistent layer positions during progressive depth expansion, addressing performance degradation issues in existing stacking approaches and demonstrating effectiveness on the SUPERB benchmark.

This research addresses a critical bottleneck in machine learning model deployment: while knowledge distillation successfully compresses large foundation models for resource-constrained environments, the training process itself remains computationally expensive. The paper tackles this efficiency gap by investigating how progressive model depth expansion during training can accelerate the distillation process without sacrificing output quality.

The significance lies in understanding how speech foundation models encode information hierarchically. Unlike generic neural networks, SFMs develop layer-specific knowledge representations where each layer captures distinct acoustic or linguistic properties. Previous stacking methods that progressively increase model depth during training improved speed but disrupted this learned layer structure, causing performance degradation. Interleaved stacking solves this by preserving the relative position of layers throughout training, maintaining the integrity of layer-specific knowledge.

For the AI industry, this advancement has practical implications for edge deployment and real-time speech applications. Faster training means quicker iteration cycles for practitioners developing speech models for low-resource scenarios—common in developing markets, mobile devices, and embedded systems. Reduced training time also lowers computational costs and environmental impact associated with model development.

The validation on SUPERB (a standard speech understanding benchmark) provides concrete evidence of effectiveness. Looking forward, this approach could extend beyond speech to other foundation models, potentially establishing interleaved stacking as a general principle for efficient transfer learning. The research opens questions about how layer-specific knowledge propagates across different model architectures and whether similar preservation strategies apply to vision or language models.

• →Interleaved stacking accelerates speech foundation model distillation by maintaining consistent layer positions during progressive depth expansion.
• →The method addresses performance degradation problems in existing stacking approaches by preserving layer-specific knowledge encoding.
• →Training acceleration directly reduces computational costs and deployment time for speech models in low-resource environments.
• →SUPERB benchmark validation demonstrates the technique's effectiveness across standardized speech understanding tasks.
• →The approach suggests broader applications for efficient transfer learning across different foundation model architectures.