  Automatic speech recognition (ASR) has become increasingly ubiquitous on
modern edge devices. Past work developed streaming End-to-End (E2E) all-neural
speech recognizers that can run compactly on edge devices. However, E2E ASR
models are prone to overfitting and have difficulties in generalizing to unseen
testing data. Various techniques have been proposed to regularize the training
of ASR models, including layer normalization, dropout, spectrum data
augmentation and speed distortions in the inputs. In this work, we present a
simple yet effective noisy training strategy to further improve the E2E ASR
model training. By introducing random noise to the parameter space during
training, our method can produce smoother models at convergence that generalize
better. We apply noisy training to improve both dense and sparse
state-of-the-art Emformer models and observe consistent WER reduction.
Specifically, when training Emformers with 90% sparsity, we achieve 12% and 14%
WER improvements on the LibriSpeech Test-other and Test-clean data set,
respectively.
