Sequence-to-Sequence (S2S) models recently started to show state-of-the-art
performance for automatic speech recognition (ASR). With these large and deep
models overfitting remains the largest problem, outweighing performance
improvements that can be obtained from better architectures. One solution to
the overfitting problem is increasing the amount of available training data and
the variety exhibited by the training data with the help of data augmentation.
In this paper we examine the influence of three data augmentation methods on
the performance of two S2S model architectures. One of the data augmentation
method comes from literature, while two other methods are our own development -
a time perturbation in the frequency domain and sub-sequence sampling. Our
experiments on Switchboard and Fisher data show state-of-the-art performance
for S2S models that are trained solely on the speech training data and do not
use additional text data.Comment: To appear in ICASSP 202