On the use of neural networks for speaker independent isolated word recognition

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