NDLI: Speech recognition using integra-normalizer and neuro-fuzzy method

Content Provider	IEEE Xplore Digital Library
Author	Sung-Soo Kim Dae-Jong Lee Keun-Chang Kwak Jang-Hwan Park Jeong-Woong Ryu
Copyright Year	2000
Description	Author affiliation: Dept. of Electr. Eng., Woosuk Univ., Chonbuk, South Korea (Sung-Soo Kim)
Abstract	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.
Starting Page	1498
Ending Page	1501
File Size	319977
Page Count	4
File Format	PDF
ISBN	0780365143
ISSN	10586393
DOI	10.1109/ACSSC.2000.911240
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-10-29
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speech recognition Shape measurement Least squares approximation Feature extraction Automatic speech recognition Computer simulation Signal processing Speech enhancement Fuzzy neural networks Automation
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Networks and Communications

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