Evolutionary eigenvoice MLLR speaker adaptation

This paper considers the problem of rapid and robust speaker adaptation in automatic speech recognition (ASR) systems. We propose an approach using combination of eigenspace-based maximum likelihood linear regression (EMLLR) and evolutionary algorithms. To find the best solution for the coefficients estimation problem, we suggest using genetic algorithm (GA) for rapid speaker adaptation. This is due to the fact that genetic algorithms are not as sensitive as expectation maximization (EM) algorithm to the amount of adaptation data. Experimental results on TIMIT database illustrate that genetic algorithm, using random individuals in first population, leads to up to 1.03% improvement in phoneme recognition rate. Moreover, we show that if the first population contains coefficients initially estimated by maximum likelihood criterion, further improvement can be achieved as well. However, the amount of adaptation data does not have considerable effect on the proposed method