Improving sequence-to-sequence speech recognition training with on-the-fly data augmentation

Understand

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.