Xref: utzoo comp.ai:5045 comp.ai.neural-nets:1105 Path: utzoo!utgpu!jarvis.csri.toronto.edu!mailrus!tut.cis.ohio-state.edu!cs.utexas.edu!rutgers!aramis.rutgers.edu!tgd From: tgd@aramis.rutgers.edu (Tom Dietterich) Newsgroups: comp.ai,comp.ai.neural-nets Subject: Re: Backpropagation applications Summary: same results on common words Keywords: Neural Networks, Efficient Learning Message-ID: Date: 12 Nov 89 22:44:04 GMT References: <1690@cod.NOSC.MIL> <77404@linus.UUCP> <13659@orstcs.CS.ORST.EDU> <1989Nov9.160406.14658@Neon.Stanford.EDU> Distribution: usa Organization: Rutgers Univ., New Brunswick, N.J. Lines: 36 From: heck@Sunburn.Stanford.EDU (Stefan P. Heck) writes According to Rumelhart in his ANN/PDP class here, Nettalk was trained on a set of the 1000 most common words rather than a random set. This run took overnight to learn. They later also did a second test using 10 000 words. I don't know for which run the accuracy figures are, but supposedly it got 87% right except on words which were irregular. The best competitor at the time was about 89% accurate. Human capability was estimated at 96%. I have also run the algorithm on the 1000 most common words. The results are quite similar to those I reported for 1000 randomly selected words. Testing is performed on the remaining 19000 words in the dictionary. WORDS LETTERS (PHON/STRESS) BITS ------------------------------------------------------------------- BP TRAIN: 76.6 94.8 97.1 97.3 99.6 120 hidden units TEST : 13.4 68.1 78.7 80.0 96.0 Sejnowski and Rosenberg also trained and tested nettalk on a corpus of connected conversational speech. I don't have access to that data, so I haven't replicated that part of their study. In my work (and in the S&R original), the 1000 most common words are presented one-at-a-time surrounded by blanks. Thomas G. Dietterich Department of Computer Science Computer Science Bldg, Room 100 Oregon State University Corvallis, OR 97331-3902