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We present a feature enhancement method that uses neural networks (NNs) to map the reverberant feature in a log-melspectral domain to its corresponding anechoic feature. The mapping is done by cascade NNs trained using Cascade2 algorithm with an implementation of segment-based normalization. Experiments using speaker identification (SID) and automatic speech recognition (ASR) systems were conducted to evaluate the method. The experiments of SID system was conducted by using our own simulated and real reverberant datasets, while the CENSREC-4 evaluation framework was used as the evaluation for the ASR system. The proposed method could remarkably improve the performance of both systems by using limited stereo data and low speaker-variant data as the training data. From the evaluation using SID, we reached 26.0% and 34.8% of error rate reduction (ERR) relative to the …