Non-Specific Person Continuous Speech Identification in Second Language using BPR

Abstract

Second language speech recognition is an important technical means of man-machine communication system. In this paper, we propose a biomimetic pattern recognition (BPR) algorithm for non specific person continuous speech identification in second language. Feature parameters are extracted directly from single number sample being segmented according to Mel cepstral way. BPR-based connected number recognition experiments show that the proposed method with better identification performance in second language than NN neural network and SVM. From non specific person continuous speech recognition experiments, we show that the BPR algorithm greatly improves word recognition accuracy and has good recognition ability without causing poorer performance in second language.

DOI: http://dx.doi.org/10.11591/telkomnika.v10i7.1550