  In Automatic Speech Recognition (ASR) systems, a recurring obstacle is the
generation of narrowly focused output distributions. This phenomenon emerges as
a side effect of Connectionist Temporal Classification (CTC), a robust sequence
learning tool that utilizes dynamic programming for sequence mapping. While
earlier efforts have tried to combine the CTC loss with an entropy maximization
regularization term to mitigate this issue, they employed a constant weighting
term on the regularization during the training, which we find may not be
optimal. In this work, we introduce Adaptive Maximum Entropy Regularization
(AdaMER), a technique that can modulate the impact of entropy regularization
throughout the training process. This approach not only refines ASR model
training but ensures that as training proceeds, predictions display the desired
model confidence.
