Neural Network for Speech Recognition Using Signal Analysis Characteristics by ${\nabla}^2G$ Operator

${\nabla}^2G$ 연산자의 신호 분석 특성을 이용한 음성 인식 신경 회로망에 관한 연구

In this paper, we propose a neural network model for speech recognition. The model consists of feature extraction parts and recognition parts. The interconnection model based on ${\Delta}^2$G operator was used for frequency analysis. Two features, global feature and local feature, were extracted from this model. Recognition parts consist of global grouping stage and local grouping stage. When the input pattern was coded by slope method, the recognition rate of speakers, A and B, was 100%. When the test was performed with the data of 9 speakers, the recognition rate of 91.4% was obtained.