Multi-objective optimisation of wavelet features for phoneme recognition

LEANDRO DANIEL VIGNOLO; HUGO LEONARDO RUFINER; DIEGO HUMBERTO MILONE

Buenos Aires

Simposio; ASAI - Simposio Argentino de Inteligencia Artificial (17º edición); 2016

SADIO

State-of-the-art speech representations provide acceptable recognition results under optimal conditions, though, their performance in adverse conditions still needs to be improved. In this direction, many advances involving wavelet processing have been reported, showing significant improvements in classification performance for different kind of signals. However, for speech signals, the problem of finding a convenient wavelet based representation is still an open challenge.This work proposes the use of a multi-objective genetic algorithm for the optimisation of a wavelet based representation of speech. The most relevant features are selected from a complete wavelet packet decomposition in order to maximise phoneme classification performance.Classification results for English phonemes, in different noise conditions, show significant improvements compared to well-known speech representations.