Speech recognition using a wavelet transform to establish fuzzy inference system through subtractive clustering and neural network (ANFIS)

Authors:
Mohamed El-Wakdy;Ehab El-Sehely;Mostafa El-Tokhy;Adel El-Hennawy
Affiliations:
-;-;-;-
Venue:
ICS'08 Proceedings of the 12th WSEAS international conference on Systems
Year:
2008

Citing 6
Cited 0

Fuzzy sets and applications

Fuzzy sets and applications
Structure identification of fuzzy model

Fuzzy Sets and Systems
Learning internal representations by error propagation

Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence

Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Local feature extraction and its applications using a library of bases

Local feature extraction and its applications using a library of bases
Wavelet-based statistical signal processing using hidden Markovmodels

IEEE Transactions on Signal Processing

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Abstract

In this paper, a proposed speech recognition algorithm is presented. This paper deals with the combination of a feature extraction by wavelet transform, subtractive clustering and adaptive neuro-fuzzy inference system (ANFIS). The feature extraction is used as input of the subtractive clustering to put the data in a group of clusters. Also it is used as an input of the neural network in ANFIS. The initial fuzzy inference system is trained by the neural network to obtain the least possible error between the desired output (target) and the fuzzy inference system (FIS) output to get the final FIS. The performance of the proposed speech recognition algorithm (SRA) using a wavelet transform and ANFIS is evaluated by different samples of speech signals- isolated words- with added background noise. The proposed speech recognition algorithm is tested using different isolated words obtaining a recognition ratio about 99%.