A Unified Cascaded Encoder ASR Model for Dynamic Model Sizes
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In this paper, we propose a dynamic cascaded encoder Automatic Speech Recognition (ASR) model, which unifies models for different deployment scenarios. Moreover, the model can significantly reduce model size and power consumption without loss of quality. Namely, with the dynamic cascaded encoder model, we explore three techniques to maximally boost the performance of each model size: 1) Use separate decoders for each sub-model while sharing the encoders; 2) Use funnel-pooling to improve the encoder efficiency; 3) Balance the size of causal and non-causal encoders to improve quality and fit deployment constraints. Overall, the proposed large-medium model has 30 smaller size and reduces power consumption by 33 cascaded encoder model. The triple-size model that unifies the large, medium, and small models achieves 37 while substantially reducing the engineering efforts of having separate models.
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