In this study, we explore an i-vector based adaptation of deep neural
network (DNN) in noisy environment. We first demonstrate the importance
of encapsulating environment and channel variability into i-vectors
for DNN adaptation in noisy conditions. To be able to obtain robust
i-vector without losing noise and channel variability information,
we investigate the use of parallel feature based i-vector extraction
for DNN adaptation. Specifically, different types of features are used
separately during two different stages of i-vector extraction namely
universal background model (UBM) state alignment and i-vector computation. To capture noise and channel-specific feature variation, the conventional MFCC features are still used for i-vector computation. However, much more robust features such as Vector Taylor Series (VTS) enhanced as well as bottleneck features are exploited for UBM state alignment. Experimental results on Aurora-4 show that the parallel feature-based i-vectors yield performance gains of up to 9.2% relative compared to a baseline DNN-HMM system and 3.3% compared to a system using conventional MFCC-based i-vectors.
Bibliographic reference. Yu, Chengzhu / Ogawa, Atsunori / Delcroix, Marc / Yoshioka, Takuya / Nakatani, Tomohiro / Hansen, John H. L. (2015): "Robust i-vector extraction for neural network adaptation in noisy environment", In INTERSPEECH-2015, 2854-2857.