Speech recognition in noisy environments: a survey
Speech Communication
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Statistical methods for speech recognition
Statistical methods for speech recognition
Noise Reduction in Speech Applications
Noise Reduction in Speech Applications
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Evolutionary Computation
Pattern Recognition Letters
Individual dimension gaussian mixture model for speaker identification
IWBRS'05 Proceedings of the 2005 international conference on Advances in Biometric Person Authentication
Evolutionary computation: comments on the history and current state
IEEE Transactions on Evolutionary Computation
Genetic wavelet packets for speech recognition
Expert Systems with Applications: An International Journal
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Evolutionary algorithms provide flexibility and robustness required to find satisfactory solutions in complex search spaces. This is why they are successfully applied for solving real engineering problems. In this work we propose an algorithm to evolve a robust speech representation, using a dynamic data selection method for reducing the computational cost of the fitness computation while improving the generalisation capabilities. The most commonly used speech representation are the mel-frequency cepstral coefficients, which incorporate biologically inspired characteristics into artificial recognizers. Recent advances have been made with the introduction of alternatives to the classic mel scaled filterbank, improving the phoneme recognition performance in adverse conditions. In order to find an optimal filterbank, filter parameters such as the central and side frequencies are optimised. A hidden Markov model is used as the classifier for the evaluation of the fitness for each individual. Experiments were conducted using real and synthetic phoneme databases, considering different additive noise levels. Classification results show that the method accomplishes the task of finding an optimised filterbank for phoneme recognition, which provides robustness in adverse conditions.