Vowel classification by a neurophysiologically parameterized auditory model

Abstract

A meaningful feature extraction is a very important challenge indispensable to allow good classification results. In Automatic Speech Recognition human performance is still superior to technical solutions. In this paper a feature extraction for sound data is presented that is perceptually motivated by the signal processing of the human auditory system. The physiological mechanisms of signal transduction in the human ear and its neural representation are described. The generated pulse spiking trains of the inner hair cells are connected to a feed forward timing artificial Hubel-Wiesel network, which is a structured computational map for higher cognitive functions as e.g. vowel recognition. According to the theory of Greenberg a signal triggers a set of delay trajectories. In the paper this is shown for classification of different vowels from several speakers.