Subband minimum classification error beamforming for speech recognition in reverberant environments

In this paper, a subband minimum classification error beamforming (S-MCEBEAM), instead of the subband likelihood maximizing beamforming (S-LIMABEAM) proposed by Seltzer, is investigated to closely integrate microphone array and speech recognizer for robust speech recognition in reverberant environments. The main idea behind this is to apply minimum classification error (MCE) criterion to directly match the goal of automatic speech recognition (ASR) and to simultaneously adjust both beamformer parameters and recognizer´s acoustic models. Experimental results on a Mandarin reverberation corpus created from Mandarin spontaneous speech corpus (TCC300) and RWCP´s sound scene database show S-MCEBEAM leads to better recognition results than S-LIMABEAM in reverberant environments.