On the use of neural networks for speaker independent isolated word recognition
Piero Demichelis, L. Fissore, Pietro Laface, Giorgio Micca, Elio Piccolo · International Conference on Acoustics, Speech, and Signal Processing · 2003
The authors present results obtained by applying the connectionist approach of multilayer perceptrons (MLPs) to three tasks of practical interest: classification of speech in terms of broad phonetic classes, speaker-independent recognition of yes/no answers through the dialed-up telephone line, and speaker-independent recognition of isolated digits through the telephone line. The first task assesses the capability of a simple MLP to generate nonlinear decision surfaces that discriminate among six broad phonetic classes. The MLP performance is actually comparable to that obtained by a hierarchical polynomial classifier. The second task deals with the sequential nature of speech. As short words like SI/NO do not give relevant problems of time alignment, the effects of different parts of the signal are taken into account by means of hidden units. A 98% recognition rate is achieved. For the third task, digital recognition, where the length of the words has a large range variation, a nonlinear time alignment is used that is performed through trace segmentation.>