Speech recognition using integra-normalizer and neuro-fuzzy method
Sung-Soo Kim, Dae-Jong Lee, Keun-Chang Kwak, Jang-Hwan Park, Jeong-Woong Ryu · 2002
This paper represents a new method of recognizing speech using the metric defined by the integra-normalizer (IN) and the neuro-fuzzy method. A codebook contains a set of feature vectors that is extracted from raw speech data. The degree of similarity between speech is measured as the distance between the speech feature vectors. The method of measuring distance between feature vectors is obtained by using the new metric presented in this paper using the IN that possesses some advantage to conventional metrics such as the metric defined to measure the least square error. With the approach used in this paper, information on the shape of the speech patterns is mapped to the feature vectors and the metric measures the difference between speech patterns considering the shape of the patterns also. The results of the computer simulation are shown for the validity of this proposed method.