clarke@csri.toronto.edu (Jim Clarke) (07/04/88)
A.I. SEMINAR, Tuesday, July 5, 11:00 am - 12:30 pm, SF3207 (SF = Sandford Fleming Building, 10 King's College Road) R. Watrous Siemens Research and Technology Laboratories University of Pennsylvania, CIS Dept. "Design, Training and Analysis of Connectionist Networks for Speech Recognition" The design of connectionist networks for speech recognition is considered in the light of the characteristics of the speech signal. A network model is defined, called the temporal flow model, which consists of simple pro- cessing elements interconnected by links which have an associated weight and time delay. The model is also general with respect to patterns of con- nectivity, and will accomodate feedback links, within and between layers. The model can be viewed as an adaptive dynamic nonlinear discriminant. The model can be trained to categorize speech input data by a method of mean-squared error minimization using gradient methods of nonlinear optimi- zation. The complete gradient is shown to be computable for networks of general connectivity. Methods for analyzing network solutions are described, in order to inter- pret and improve model performance. These methods include link weight and unit activation analysis, parameter correlation, residual error and sensi- tivity analysis. The adequacy of the model for acoustic phonetic speech recognition is explored along major axes of the acoustic phonetic space as defined by the physiological theory of phonetics (Peterson and Shoup, 1966). Preliminary results are reported for optimized networks for several experiments for a single male speaker, using 10 to 40 training utterances, tested on 60 to 90 repetitions of each test token. The phoneme discrimination accuracies range from 97% to 99%. -- Jim Clarke -- Dept. of Computer Science, Univ. of Toronto, Canada M5S 1A4 (416) 978-4058 BITNET,CSNET: clarke@csri.toronto.edu CDNNET: clarke@csri.toronto.cdn UUCP: {allegra,cornell,decvax,linus,utzoo}!utcsri!clarke