Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
A New Codebook Training Algorithm for VQ-Based Speaker Recognition
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
A Novel Fuzzy Approach to Recognition of Online Persian Handwriting
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
Wavelet neural networks for function learning
IEEE Transactions on Signal Processing
Speaker Model Clustering for Efficient Speaker Identification in Large Population Applications
IEEE Transactions on Audio, Speech, and Language Processing
An integrated approach to fuzzy learning vector quantization and fuzzy c-means clustering
IEEE Transactions on Fuzzy Systems
Fuzzy wavelet networks for function learning
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Image Processing
Fuzzy algorithms for learning vector quantization
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Analysis and synthesis of feedforward neural networks using discrete affine wavelet transformations
IEEE Transactions on Neural Networks
Accuracy analysis for wavelet approximations
IEEE Transactions on Neural Networks
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This work evaluates the performance of speaker verification system based on Wavelet based Fuzzy Learning Vector Quantization (WLVQ) algorithm. The parameters of Gaussian mixture model (GMM) are designed using this proposed algorithm. Mel Frequency Cepstral Coefficients (MFCC) are extracted from the speech data and vector quantized through Wavelet based FLVQ algorithm. This algorithm develops a multi resolution codebook by updating both winning and nonwinning prototypes through an unsupervised learning process. This codebook is used as mean vector of GMM. The other two parameters, weight and covariance are determined from the clusters formed by the WLVQ algorithm. The multi resolution property of wavelet transform and ability of FLVQ in regulating the competition between prototypes during learning are combined in this algorithm to develop an efficient codebook for GMM. Because of iterative nature of Expectation Maximization (EM) algorithm, the applicability of alternative training algorithms is worth investigation. In this work, the performance of speaker verification system using GMM trained by LVQ, FLVQ and WLVQ algorithms are evaluated and compared with EM algorithm. FLVQ and WLVQ based training algorithms for modeling speakers using GMM yields better performance than EM based GMM.