SPEECH RECOGNITION BY A SELF-ORGANIZING FEATURE FINDER
Solomon Lerner, J.R. Deller · International Journal of Neural Systems · 1991
A self-organizing neural network is presented which automatically learns the number and type of spectral features from speech examples. The learning algorithm is analyzed with respect to its convergence and stability properties. The “strength” of the presence of the learned features is registered by the network to effect recognition of further speech presentations. The network consists of two layers of feature detectors, each layer of which is self-organized, and the outputs of the second layer are time-aligned in the present design using dynamic time warping. The significance of the two-layer structure, as well as general architectural advantages of the network, are discussed. Results of experiments involving various isolated word recognition tasks, including single and multi-speaker training and recognition, and the recognition of speech of a nonverbal individual, are reported.