Robust speech recognition using beamforming with adaptive microphone gains and multichannel noise reduction
Shengkui Zhao, Xiong Xiao, Zhaofeng Zhang, Thi Ngoc Tho Nguyen, Xionghu Zhong, Bo Ren, Longbiao Wang, Douglas L. Jones, Eng Siong Chng, Haizhou Li · 2015
This paper presents a robust speech recognition system using a microphone array for the 3rd CHiME Challenge. A minimum variance distortionless response (MVDR) beamformer with adaptive microphone gains is proposed for robust beamforming. Two microphone gain estimation methods are studied using the speech-dominant time-frequency bins. A multichannel noise reduction (MCNR) postprocessing is also proposed to further reduce the interference in the MVDR processed signal. Experimental results for the ChiME-3 challenge show that both the proposed MVDR beamformer with microphone gains and the MCNR postprocessing improve the speech recognition performance significantly. With the state-of-the-art deep neural network (DNN) based acoustic model, our system achieves a word error rate (WER) of 11.67% on the real test data of the evaluation set.