Robust feature extraction in a variety of input devices on the basis of ETSI standard DSR front-end
Satoru Tsuge, Shingo Kuroiwa, Masami Shishibori, Fuji Ren, Kenji Kita · 2002
This paper reports an evaluation of European Telecommunications Standards Institute (ETSI) standard Distributed Speech Recognition (DSR) front-end through continuous word recognition on a Japanese speech corpus and proposes a method, the Bias Removal Method (BRM), that reduces the distortion between feature vector and VQ codebook. Experimental results show that using nonquantized features in acoustic model training procedure can improve the recognition performance of DSR fornt-end features and that the proposed method can improve recognition performances of DSR front-end feature.