  Speech enhancement (SE) is proved effective in reducing noise from noisy
speech signals for downstream automatic speech recognition (ASR), where
multi-task learning strategy is employed to jointly optimize these two tasks.
However, the enhanced speech learned by SE objective may not always yield good
ASR results. From the optimization view, there sometimes exists interference
between the gradients of SE and ASR tasks, which could hinder the multi-task
learning and finally lead to sub-optimal ASR performance. In this paper, we
propose a simple yet effective approach called gradient remedy (GR) to solve
interference between task gradients in noise-robust speech recognition, from
perspectives of both angle and magnitude. Specifically, we first project the SE
task's gradient onto a dynamic surface that is at acute angle to ASR gradient,
in order to remove the conflict between them and assist in ASR optimization.
Furthermore, we adaptively rescale the magnitude of two gradients to prevent
the dominant ASR task from being misled by SE gradient. Experimental results
show that the proposed approach well resolves the gradient interference and
achieves relative word error rate (WER) reductions of 9.3% and 11.1% over
multi-task learning baseline, on RATS and CHiME-4 datasets, respectively. Our
code is available at GitHub.
