The Ntu-adsc Systems for Reverberation Challenge

This paper describes our speech enhancement and recognition systems developed for the Reverberation Challenge 2014. To enhance the noisy and reverberant speech for human listening, besides using conventional methods such as delay and sum beamformer and late reverberation reduction by spectral subtraction, we also studied a novel learning-based speech enhancement. Specifically, we train deep neural networks (DNN) to map reverberant spectrogram to the corresponding clean spectrogram by using parallel data of clean and reverberant speech. Results show that the trained DNN is able to reduce reverberation significantly for unseen test data. For the speech recognition task, when parallel data is available, we train a DNN tomap reverberant features to clean features, following the same spirit as the DNN-based speech enhancement. Results show that the DNN-based feature compensation improves speech recognition performance even when a DNN acoustic model is already used, showing the benefit of explicitly cleansing the features. When parallel data is not available in the clean condition training scheme, we focus on reducing the training-test mismatch by using our proposed cross transform feature adaptation that uses both temporal and spectral information. The cross transform works complementarily with traditional model adaptation.