Phoneme recognition with an artificial neural network
Kjell O. E. Elenius, György Takács · 1991
An artificial neural network has been trained to recog-nize phonemes using the error back-propagation tech-nique. First a coarse feature network is trained to extract seven quasi-phonetic features from the spectral frames of a Bark-scaled filter bank. The outputs of this net and the spectral outputs of the filter bank were input to a phoneme recognition net. The coarse features were recognized with 80 %- 93 % accuracy. Using manual segmentation the phone recognition rate was 64 % and in 82 % of the cases, the correct phone was among the best three candidates.