An auditory model based vowel classification

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 motivated by the neural 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 auditory nerve fibers 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 former cochlea studies a signal triggers a set of delay trajectories on the basilar membrane, which will be projected further to connecting structures. In our approach this phenomenon is employed for classification of vowels from different speakers.