  This work presents our end-to-end (E2E) automatic speech recognition (ASR)
model targetting at robust speech recognition, called Integraded speech
Recognition with enhanced speech Input for Self-supervised learning
representation (IRIS). Compared with conventional E2E ASR models, the proposed
E2E model integrates two important modules including a speech enhancement (SE)
module and a self-supervised learning representation (SSLR) module. The SE
module enhances the noisy speech. Then the SSLR module extracts features from
enhanced speech to be used for speech recognition (ASR). To train the proposed
model, we establish an efficient learning scheme. Evaluation results on the
monaural CHiME-4 task show that the IRIS model achieves the best performance
reported in the literature for the single-channel CHiME-4 benchmark (2.0% for
the real development and 3.9% for the real test) thanks to the powerful
pre-trained SSLR module and the fine-tuned SE module.
