ApproBiVT: Lead ASR Models to Generalize Better Using Approximated
  Bias-Variance Tradeoff Guided Early Stopping and Checkpoint Averaging
Fangyuan Wang Ming Hao Yuhai Shi Bo Xu
The conventional recipe for Automatic Speech Recognition (ASR) models is to
1) train multiple checkpoints on a training set while relying on a validation
set to prevent overfitting using early stopping and 2) average several last
checkpoints or that of the lowest validation losses to obtain the final model.
In this paper, we rethink and update the early stopping and checkpoint
averaging from the perspective of the bias-variance tradeoff. Theoretically,
the bias and variance represent the fitness and variability of a model and the
tradeoff of them determines the overall generalization error. But, it's
impractical to evaluate them precisely. As an alternative, we take the training
loss and validation loss as proxies of bias and variance and guide the early
stopping and checkpoint averaging using their tradeoff, namely an Approximated
Bias-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 and
AISHELL-2, respectively.