Comparing distributed and local neural classifiers for the recognition of Japanese phonemes

The comparative performances of distributed and local neural networks for the speech recognition problem is investigated. Distributed networks´ hidden units use the signoid nonlinearity with global response. We have used the backpropagation rule with three error measures: mean square error, cross entropy, and combinational performance. The hidden units of local networks respond only to inputs in a certain local region in the input space. We used k-nearest neighbor (kNN), Gaussian-based kNN, learning vector quantization, and grow and learn methods. Phoneme recognition experiments were conducted using the /b,d,g,m,n,N/ set of the Japanese vocabulary for the speaker dependent case. Three criteria are considered for comparison: correct classification of the test set, network size, and learning time.