finin@BIGBURD.PRC.UNISYS.COM.UUCP (10/27/87)
AI Seminar UNISYS Knowledge Systems Paoli Research Center Paoli PA SPEECH RECOGNITION USING CONNECTIONIST NETWORKS Raymond Watrous Siemens Corporate Research and University of Pennsylvania The thesis of this research is that connectionist networks are adequate models for the problem of acoustic phonetic speech recognition by computer. Adequacy is defined as suitably high recognition performance on a representative set of speech recognition problems. Six acoustic phonetic problems are selected and discussed in relation to a physiological theory of phonetics. It is argued that the selected tasks are sufficiently representative and difficult to constitute a reasonable test of adequacy. A connectionist network is a fine-grained parallel distributed processing configuration, in which simple processing elements are interconnected by simple links. A connectionist network model for speech recognition has been defined called the TEMPORAL FLOW MODEL. The model incorporates link propagation delay and internal feedback to express temporal relationships. It has been shown that temporal flow models can be 'trained' to perform successfully some speech recognition tasks. A method of 'learning' using techniques of numerical nonlinear optimization has been demonstrated for the minimal pair "no/go", and voiced stop consonant discrimination in the context of various vowels. Methods for extending these results to new problems are discussed. 10:00am Wednesday, November 4, 1987 Cafeteria Conference Room Unisys Paloi Research Center Route 252 and Central Ave. Paoli PA 19311 -- non-UNISYS visitors who are interested in attending should -- -- send email to finin@prc.unisys.com or call 215-648-7446 --