Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Fundamentals of speech recognition
Fundamentals of speech recognition
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Statistical methods for speech recognition
Statistical methods for speech recognition
Discrete Time Processing of Speech Signals
Discrete Time Processing of Speech Signals
Comparison of different implementations of MFCC
Journal of Computer Science and Technology
Hidden Markov Models for Speech Recognition
Hidden Markov Models for Speech Recognition
Dynamic Training Subset Selection for Supervised Learning in Genetic Programming
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Data Driven Design of Filter Bank for Speech Recognition
TSD '01 Proceedings of the 4th International Conference on Text, Speech and Dialogue
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Evolutionary Computation
Pattern Recognition Letters
Multi filter bank approach for speaker verification based on genetic algorithm
NOLISP'07 Proceedings of the 2007 international conference on Advances in nonlinear speech processing
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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Mel-frequency cepstral coefficients have long been the most widely used type of speech representation. They were introduced to incorporate biologically inspired characteristics into artificial speech recognizers. Recently, the introduction of new alternatives to the classic mel-scaled filterbank has led to improvements in the performance of phoneme recognition in adverse conditions. In this work we propose a new bioinspired approach for the optimization of the filterbanks, in order to find a robust speech representation. Our approach--which relies on evolutionary algorithms--reduces the number of parameters to optimize by using spline functions to shape the filterbanks. The success rates of a phoneme classifier based on hidden Markov models are used as the fitness measure, evaluated over the well-known TIMIT database. The results show that the proposed method is able to find optimized filterbanks for phoneme recognition, which significantly increases the robustness in adverse conditions.