GIF-SP: GA-based Informative Feature for Noisy Speech Recognition

Abstract-This paper proposes a novel discriminative feature extraction method. The method consists of two stages; in the first stage, a classifier is built for each class, which categorizes an input vector into a certain class or not. From all the parameters of the classifiers, a first transformation can be formed. In the second stage, another transformation that generates a feature vector is subsequently obtained to reduce the dimension and enhance recognition ability. These transformations are computed applying genetic algorithm. In order to evaluate the performance of the proposed feature, speech recognition experiments were conducted. Results in clean training condition shows that GIF greatly improves recognition accuracy compared to conventional MFCC in noisy environments. Multi-condition results also clarifies that out proposed scheme is robust against differences of conditions

Discriminative transformation for speech features based on genetic algorithm and HMM likelihoods

Hidden Markov Model (HMM) is a well-known classification approach which its parameters are conventionally learned using maximum likelihood (ML) criterion based on expectation maximization algorithm. Improving of parameter learning beyond ML has been performed based on the concept of discrimination among classes in contrast to maximizing likelihood of each individual class. In this paper, we propose a discriminative feature transformation method based on genetic algorithm, to increase Hidden Markov Model likelihoods in its training phase for a better class discrimination. The method is evaluated for phoneme recognition on clean and noisy TIMIT. Experimental results demonstrate that the proposed transformation method results in higher phone recognition rate than well-known feature transformations methods and conventional HMM learning algorithm based on ML criterion