An ideal hidden-activation mask for deep neural networks based noise-robust speech recognition
Bo Li, Khe Chai Sim · 2014
Deep neural networks (DNNs) are capable of modeling large acoustic variations. However, the performance on noisy data is still below humans' expectations. In this work, we present an ideal hidden-activation masking (IHM) approach to improve their noise robustness. This IHM is inspired by the existing spectral masking techniques. Instead of masking away the noise-dominant components in the spectral domain, we propose to discard DNNs' inconsistent hidden activations. The IHM is computed from the parallel data to identify hidden units that are immune to environment noise. DNNs then utilize it to improve their prediction robustness with the noise-invariant activations. Experimental results on the Aurora4 task have shown that the proposed IHM is both effective in reducing noise variations and robust to mask estimation errors.