Abstract
A neural network that employs unsupervised learning was used on the output of a neurophysiologically based model of the auditory periphery [K. L. Payton, J. Acoust. Soc. Am. 83, 145–162 (1988)] to perform phoneme recognition. The auditory model incorporates steps describing the conversion from the acoustic pressure‐wave signal at the eardrum to the time course activity in auditory neurons. The model can process arbitrary time domain waveforms and yields the predicated neural firing rate. Continuous speech from ten speakers (three female and seven male) taken from the TIMIT database [W. Fisher et al., J. Acoust. Soc. Am. Suppl. 1 81, S92 (1987)] was processed through the auditory model for this experiment. The average firing rate of 20 channels from the auditory model was used to train a Kohonen self‐organizing feature map. The resulting context‐independent phoneme recognition performance (30% correct) was comparable to that of the SPHINX System [K. F. Lee and H. W. Hon, IEEE Trans. Acoust. Speech Signal P...
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
