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 recognition-oriented speech
enhancement (ROSE) framework is proposed to improve speech intelligibility and
also advance ASR accuracy, which serves as a plug-and-play tool in ATC
scenarios and does not require additional retraining of the ASR model.
Specifically, an encoder-decoder-based U-Net framework is proposed to eliminate
the radio speech echo based on the real-world collected corpus. By
incorporating the SE-oriented and ASR-oriented loss, ROSE is implemented in a
multi-objective manner by learning shared representations across the two
optimization objectives. An attention-based skip-fusion (ABSF) mechanism is
applied to skip connections to refine the features. A channel and sequence
attention (CSAtt) block is innovatively designed to guide the model to focus on
informative representations and suppress disturbing features. The experimental
results show that the ROSE significantly outperforms other state-of-the-art
methods for both the SE and ASR tasks. In addition, the proposed approach can
contribute to the desired performance improvements on public datasets.

提出了一种基于编码器 - 解码器的 U-Net 框架的识别导向语音增强（ROSE）框架，以消除空中交通管制 (ATC) 领域的无线电语音回声，并提高语音可懂性和自动语音识别 (ASR) 准确性。ROSE 在 ATC 场景中作为即插即用工具，无需对 ASR 模型进行额外的重新训练。