Sequence Noise Injected Training For End-To-End Speech Recognition

We present a simple noise injection algorithm for training end-to-end ASR models which consists in adding to the spectra of training utterances the scaled spectra of random utterances of comparable length. We conjecture that the sequence information of the "noise" utterances is important and verify this via a contrast experiment where the frames of the utterances to be added are randomly shuffled. Experiments for both CTC and attention-based models show that the proposed scheme results in up to 9% relative word error rate improvements ( depending on the model and test set) on the Switchboard 300 hours English conversational telephony database. Additionally, we set a new benchmark for attention-based encoder-decoder models on this corpus.