Multi-objective optimisation of wavelet features for phoneme recognition

Abstract

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 kinds of signals. However, for speech signals, the problem of finding a convenient wavelet-based representation is still an open challenge. This study 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 with well-known speech representations.