  Automatic speech recognition (ASR) systems degrade significantly under noisy
conditions. Recently, speech enhancement (SE) is introduced as front-end to
reduce noise for ASR, but it also suppresses some important speech information,
i.e., over-suppression. To alleviate this, we propose a dual-path style
learning approach for end-to-end noise-robust speech recognition (DPSL-ASR).
Specifically, we first introduce clean speech feature along with the fused
feature from IFF-Net as dual-path inputs to recover the suppressed information.
Then, we propose style learning to map the fused feature close to clean
feature, in order to learn latent speech information from the latter, i.e.,
clean "speech style". Furthermore, we also minimize the distance of final ASR
outputs in two paths to improve noise-robustness. Experiments show that the
proposed approach achieves relative word error rate (WER) reductions of 10.6%
and 8.6% over the best IFF-Net baseline, on RATS and CHiME-4 datasets
respectively.
