Evolutionary Splines for Cepstral Filterbank Optimization in Phoneme Classification

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.