Abstract
This paper reports an evaluation of European Telecommunications Standards Institute (ETSI) standard Distributed Speech Recognition (DSR) front-end through continuous speech recognition on a Japanese speech corpus and proposes methods, the Bias Removal Methods (BRMs), that reduce the distortion between feature parameters and the VQ codebook. Experimental results show that (1) using non-quantized features in an acoustic model training procedure can improve the recognition performance of DSR front-end features and (2) broadening the analysis band can improve the recognition performance for the same bitrate. The proposed method can improve the recognition performance in DSR condition. Notably, we observed an 18% relative improvement in the error rate using the proposed method under mismatch of channel characteristic conditions.
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