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.
['Shengkui Zhao', 'Xiong Xiao', 'Zhaofeng Zhang', 'Thi Ngoc Tho Nguyen', 'Xionghu Zhong', 'Bo Ren', 'Longbiao Wang', 'Douglas L. Jones', 'Eng Siong Chng', 'Haizhou Li']
Robust speech recognition using beamforming with adaptive microphone gains and multichannel noise reduction