Application of a modified neural fuzzy network and an improved genetic algorithm to speech recognition

Authors:
K. F. Leung;F. H. F. Leung;H. K. Lam;S. H. Ling
Affiliations:
The Hong Kong Polytechnic University, Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering, Hung Hom, Kowloon, Hong Kong, China;The Hong Kong Polytechnic University, Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering, Hung Hom, Kowloon, Hong Kong, China;King’s College London, Division of Engineering, Strand, London, UK;The Hong Kong Polytechnic University, Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering, Hung Hom, Kowloon, Hong Kong, China
Venue:
Neural Computing and Applications
Year:
2007

Citing 0
Cited 8

Evolving Committees of Support Vector Machines

MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
A feature selection technique for generation of classification committees and its application to categorization of laryngeal images

Pattern Recognition
Using the patient's questionnaire data to screen laryngeal disorders

Computers in Biology and Medicine
Screening Paper Runnability in a Web-Offset Pressroom by Data Mining

ICDM '09 Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects
Combining image, voice, and the patient's questionnaire data to categorize laryngeal disorders

Artificial Intelligence in Medicine
Selecting features from multiple feature sets for SVM committee-based screening of human larynx

Expert Systems with Applications: An International Journal
Screening web breaks in a pressroom by soft computing

Applied Soft Computing
Hierarchical ANN system for stuttering identification

Computer Speech and Language

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents the recognition of speech commands using a modified neural fuzzy network (NFN). By introducing associative memory (the tuner NFN) into the classification process (the classifier NFN), the network parameters could be made adaptive to changing input data. Then, the search space of the classification network could be enlarged by a single network. To train the parameters of the modified NFN, an improved genetic algorithm is proposed. As an application example, the proposed speech recognition approach is implemented in an eBook experimentally to illustrate the design and its merits.