RECOGNITION OF ISOLATED WORDS BASED ON PSYCHOACOUSTICS AND NEUROBIOLOGY

A simple neural network for isolated word recognition constructed under consideration of neurobiological and psychoacoustical observations is described. The biologically motivated preprocessing of the speech signals includes transforming frequency to critical band-rate and power to loudness, contrasting the spectrograms and extracting temporal and spectral features. It is shown that the different stages of preprocessing of the speech signal increase recognition rates significantly and are essential to achieve faultless recognition of a small vocabulary. In addition, the network is able to recognize simultaneously spoken words without any change of its architecture. Thus, it represents a concept to solve one of the most difficult figure-ground-problems in speech research without using conventional techniques like directional separation of stereophonically recorded speech or fundamental frequency tracking. © 1990.