Fundamentals of speech recognition
Fundamentals of speech recognition
Speaker identification and verification using Gaussian mixture speaker models
Speech Communication
Genetic Algorithms for Control and Signal Processing
Genetic Algorithms for Control and Signal Processing
Optimization of HMM by a Genetic Algorithm
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 3 - Volume 3
On the application of mixture AR hidden Markov models to textindependent speaker recognition
IEEE Transactions on Signal Processing
An efficient digital VLSI implementation of Gaussian mixture models-based classifier
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Automated speech analysis applied to laryngeal disease categorization
Computer Methods and Programs in Biomedicine
GA approaches to HMM optimization for automatic speech recognition
MCPR'12 Proceedings of the 4th Mexican conference on Pattern Recognition
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Gaussian mixture model (GMM) has been widely used for modeling speakers. In speaker identification, one major problem is how to generate a set of GMMs for identification purposes based upon the training data. Due to the hill-climbing characteristic of the maximum likelihood (ML) method, any arbitrary estimate of the initial model parameters will usually lead to a sub-optimal model in practice. To resolve this problem, this paper proposes a hybrid training method based on genetic algorithm (GA). It utilizes the global searching capability of GA and combines the effectiveness of the ML method. Experimental results based on TI46 and TIMIT showed that this hybrid GA could obtain more optimized GMMs and better results than the simple GA and the traditional ML method.