  Noise robustness is critical when applying automatic speech recognition (ASR)
in real-world scenarios. One solution involves the used of speech enhancement
(SE) models as the front end of ASR. However, neural network-based (NN-based)
SE often introduces artifacts into the enhanced signals and harms ASR
performance, particularly when SE and ASR are independently trained. Therefore,
this study introduces a simple yet effective SE post-processing technique to
address the gap between various pre-trained SE and ASR models. A bridge module,
which is a lightweight NN, is proposed to evaluate the signal-level information
of the speech signal. Subsequently, using the signal-level information, the
observation addition technique is applied to effectively reduce the
shortcomings of SE. The experimental results demonstrate the success of our
method in integrating diverse pre-trained SE and ASR models, considerably
boosting the ASR robustness. Crucially, no prior knowledge of the ASR or speech
contents is required during the training or inference stages. Moreover, the
effectiveness of this approach extends to different datasets without
necessitating the fine-tuning of the bridge module, ensuring efficiency and
improved generalization.
