Evolutionary Splines for Cepstral Filterbank Optimization in Phoneme Classification

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 wor...

Genetic Optimization of Cepstrum Filterbank for Phoneme Classification

Some of the most commonly used speech representations, such as mel-frequency cepstral coefficients, incorporate biologically inspired characteristics into artificial systems. Recent advances have been introduced modifying the shape and distribution of the traditional perceptually scaled filterbank, commonly used for feature extraction. Some alternatives to the classic mel scaled filterbank have...

Fuzzy decision fusion of complementary experts based on evolutionary cepstral coefficients for phoneme recognition

Evolutionary cepstral coefficients

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 impro...

Ensemble Methods for Phoneme Classification

This paper investigates a number of ensemble methods for improving the performance of phoneme classification for use in a speech recognition system. Two ensemble methods are described; boosting and mixtures of experts, both in isolation and in combination. Results are presented on two speech recognition databases: an isolated word database and a large vocabulary continuous speech database. Thes...