 in real-world scenarios. One solution involves the used of speech enhancement (SE) models as the front end of ASR. However%2C neural network-based (NN-based) SE often introduces artifacts into the enhanced signals and harms ASR performance%2C particularly when SE and ASR are independently trained. Therefore%2C 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%2C which is a lightweight NN%2C is proposed to evaluate the signal-level information of the speech signal. Subsequently%2C using the signal-level information%2C 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%2C considerably boosting the ASR robustness. Crucially%2C no prior knowledge of the ASR or speech contents is required during the training or inference stages. Moreover%2C the effectiveness of this approach extends to different datasets without necessitating the fine-tuning of the bridge module%2C ensuring efficiency and improved generalization.?resize=650,400 "Bridging The Gap: Integrating Pre-trained Speech Enhancement And Recognition Models For Robust ...")
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Automatic Speech Recognition Post-Processing For Readability Task Dataset And A Two-Stage Pre ...
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Top Automatic Speech Recognition (ASR) Models in 2025 | Shunya Labs Pingala V1 vs IBM/Microsoft
Top Automatic Speech Recognition (ASR) Models in 2025 | Shunya Labs Pingala V1 vs IBM/Microsoft
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 in real-world scenarios. One solution involves the used of speech enhancement (SE) models as the front end of ASR. However%2C neural network-based (NN-based) SE often introduces artifacts into the enhanced signals and harms ASR performance%2C particularly when SE and ASR are independently trained. Therefore%2C 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%2C which is a lightweight NN%2C is proposed to evaluate the signal-level information of the speech signal. Subsequently%2C using the signal-level information%2C 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%2C considerably boosting the ASR robustness. Crucially%2C no prior knowledge of the ASR or speech contents is required during the training or inference stages. Moreover%2C the effectiveness of this approach extends to different datasets without necessitating the fine-tuning of the bridge module%2C ensuring efficiency and improved generalization.?resize=91,91 "Bridging The Gap: Integrating Pre-trained Speech Enhancement And Recognition Models For Robust ...")
![[论文审查] Bridging The Gap: Integrating Pre-trained Speech Enhancement And Recognition Models For ...](https://i0.wp.com/moonlight-paper-snapshot.s3.ap-northeast-2.amazonaws.com/arxiv/bridging-the-gap-integrating-pre-trained-speech-enhancement-and-recognition-models-for-robust-speech-recognition-2.png?resize=91,91 "[论文审查] Bridging The Gap: Integrating Pre-trained Speech Enhancement And Recognition Models For ...")
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