Yoshua Bengio, Renato de Mori, Régis Cardin
We attempt to combine neural networks with knowledge from speech science to build a speaker independent speech recogni(cid:173) tion system. This knowledge is utilized in designing the preprocessing, input coding, output coding, output supervision and architectural constraints. To handle the temporal aspect of speech we combine delays, copies of activations of hidden and output units at the input level, and Back-Propagation for Sequences (BPS), a learning algorithm for networks with local self-loops. This strategy is demonstrated in several experi(cid:173) ments, in particular a nasal discrimination task for which the application of a speech theory hypothesis dramatically im(cid:173) proved generalization.