Comparing distributed and local neural classifiers for the recognition of Japanese phonemes
Ruhan Alpaydin, Uğur Ünlüakin, Fikret Sadik Gürgen, Ethem Alpaydın · 2005
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.