  Radio speech echo is a specific phenomenon in the air traffic control (ATC)
domain, which degrades speech quality and further impacts automatic speech
recognition (ASR) accuracy. In this work, a time-domain recognition-oriented
speech enhancement (ROSE) framework is proposed to improve speech
intelligibility and also advance ASR accuracy based on convolutional
encoder-decoder-based U-Net framework, which serves as a plug-and-play tool in
ATC scenarios and does not require additional retraining of the ASR model.
Specifically, 1) In the U-Net architecture, an attention-based skip-fusion
(ABSF) module is applied to mine shared features from encoders using an
attention mask, which enables the model to effectively fuse the hierarchical
features. 2) A channel and sequence attention (CSAtt) module is innovatively
designed to guide the model to focus on informative features in dual parallel
attention paths, aiming to enhance the effective representations and suppress
the interference noises. 3) Based on the handcrafted features, ASR-oriented
optimization targets are designed to improve recognition performance in the ATC
environment by learning robust feature representations. By incorporating both
the SE-oriented and ASR-oriented losses, ROSE is implemented in a
multi-objective learning manner by optimizing shared representations across the
two task objectives. The experimental results show that the ROSE significantly
outperforms other state-of-the-art methods for both the SE and ASR tasks, in
which all the proposed improvements are confirmed by designed experiments. In
addition, the proposed approach can contribute to the desired performance
improvements on public datasets.
