Speech recognition by an artificial neural network using findings on the afferent auditory system

An artificial neural network which uses anatomical and physiological findings on the afferent pathway from the ear to the cortex is presented and the roles of the constituent functions in recognition of continuous speech are examined. The network deals with successive spectra of speech sounds by a cascade of several neural layers: lateral excitation layer (LEL), lateral inhibition layer (LIL), and a pile of feature detection layers (FDL's). These layers are shown to be effective for recognizing spoken words. Namely, first, LEL reduces the distortion of sound spectrum caused by the pitch of speech sounds. Next, LIL emphasizes the major energy peaks of sound spectrum, the formants. Last, FDL's detect syllables and words in successive formants, where two functions, time-delay and strong adaptation, play important roles: time-delay makes it possible to retain the pattern of formant changes for a period to detect spoken words successively; strong adaptation contributes to removing the time-warp of formant changes. Digital computer simulations show that the network detect isolated syllables, isolated words, and connected words in continuous speech, while reproducing the fundamental responses found in the auditory system such as ON, OFF, ON-OFF, and SUSTAINED patterns.