Speaker identification using neural networks and wavelets

Abstract

Multiresolution decomposition and pattern-recognition techniques enable identification in noisy environments. The "cocktail party" effect describes the phenomenon in which humans can selectively focus attention to one sound source among competing sound sources. This is an ability that is hampered for hearing-impaired individuals. In this article, the authors present an off-line system that uses wavelets to generate multiresolution time-frequency features that characterize the speech waveform to successfully identify a speaker in the presence of competing speakers. This system is successful for short utterances and has also been applied to interspeaker speech recognition. The authors also discuss ALOPEX, which is an optimization paradigm that incorporates the above-mentioned features into a pattern-recognition system through template matching or connectivity weight updating in a feedforward artificial neural network.