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The conventional recipe for Automatic Speech Recognition (ASR) models is to1) train multiple checkpoints on a training set while relying on a validationset to prevent overfitting using early stopping and 2) average several lastcheckpoints or that of the lowest validation losses to obtain the final model.In this paper, we rethink and update the early stopping and checkpointaveraging from the perspective of the bias-variance tradeoff. Theoretically,the bias and variance represent the fitness and variability of a model and thetradeoff of them determines the overall generalization error. But, it'simpractical to evaluate them precisely. As an alternative, we take the trainingloss and validation loss as proxies of bias and variance and guide the earlystopping and checkpoint averaging using their tradeoff, namely an ApproximatedBias-Variance Tradeoff (ApproBiVT). When evaluating with advanced ASR models,our recipe provides 2.5%-3.7% and 3.1%-4.6% CER reduction on the AISHELL-1 andAISHELL-2, respectively.

ApproBiVT: Lead ASR Models to Generalize Better Using Approximated Bias-Variance Tradeoff Guided Early Stopping and Checkpoint Averaging