A generative-discriminative hybrid approach to multi-channel noise reduction for robust automatic speech recognition

In the recent years, discriminative models have become a very attractive utility and gained a lot of attention in the speech research community, encompassing both front and back-end methods, thanks to their prominent discriminative power and the availability of improved training strategies. When it comes to the recognition of speech that is distorted by highly non-stationary environmental noise, robust front and backend methods are required in order to achieve a satisfactorily high speech recognition performance. Furthermore, when dealing with severe noise conditions, multi-channel front-end methods can be advantageous for suppressing environmental background noise, as compared to single-channel methods. In this work, we improve an existing multi-channel noise reduction approach, referred to as DOminance-based Loca-tional and Power-spectral cHaracteristics INtegration (DOLPHIN), by using a generative-discriminative hybrid model, that makes use of spatial and spectral features. We show that the proposed method outperforms the existing DOLPHIN approach, which is solely based on generative models, in terms of the word error rate reduction achieved on the CHiME-3 challenge data.