Adversarial joint training with self-attention mechanism for robust end-to-end speech recognition
Journal: EURASIP Journal on Audio Speech and Music Processing
Year: 2021

Abstract Lately, the self-attention mechanism has marked a new milestone in the field of automatic speech recognition (ASR). Nevertheless, its performance is susceptible to environmental intrusions as the system predicts the next output symbol depending on the full input sequence and the previous predictions. A popular solution for this problem is adding an independent speech enhancement module as the front-end. Nonetheless, due to being trained separately from the ASR module, the independent en