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. © 2004 Elsevier Ltd. All rights reserved.
|Journal||Engineering Applications of Artificial Intelligence|
|Publication status||Published - Feb 2005|
Bibliographical noteThis work is supported by City University of Hong Kong Strategic Grant Number 7001488.
- Gaussian mixture model
- Genetic algorithm
- Speaker identification