%20in%20real-world%20scenarios.%20One%20solution%20involves%20the%20used%20of%20speech%20enhancement%20(SE)%20models%20as%20the%20front%20end%20of%20ASR.%20However%2C%20neural%20network-based%20(NN-based)%20SE%20often%20introduces%20artifacts%20into%20the%20enhanced%20signals%20and%20harms%20ASR%20performance%2C%20particularly%20when%20SE%20and%20ASR%20are%20independently%20trained.%20Therefore%2C%20this%20study%20introduces%20a%20simple%20yet%20effective%20SE%20post-processing%20technique%20to%20address%20the%20gap%20between%20various%20pre-trained%20SE%20and%20ASR%20models.%20A%20bridge%20module%2C%20which%20is%20a%20lightweight%20NN%2C%20is%20proposed%20to%20evaluate%20the%20signal-level%20information%20of%20the%20speech%20signal.%20Subsequently%2C%20using%20the%20signal-level%20information%2C%20the%20observation%20addition%20technique%20is%20applied%20to%20effectively%20reduce%20the%20shortcomings%20of%20SE.%20The%20experimental%20results%20demonstrate%20the%20success%20of%20our%20method%20in%20integrating%20diverse%20pre-trained%20SE%20and%20ASR%20models%2C%20considerably%20boosting%20the%20ASR%20robustness.%20Crucially%2C%20no%20prior%20knowledge%20of%20the%20ASR%20or%20speech%20contents%20is%20required%20during%20the%20training%20or%20inference%20stages.%20Moreover%2C%20the%20effectiveness%20of%20this%20approach%20extends%20to%20different%20datasets%20without%20necessitating%20the%20fine-tuning%20of%20the%20bridge%20module%2C%20ensuring%20efficiency%20and%20improved%20generalization.)
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