Author: Ratko V Tomic
Date: 15:12:47 10/01/99
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That's interesting. Several years ago I was trying to use neural nets for speech recognition (as a consulting job) and had abandoned it due to poor performance (regular statistical matching recognized better and much faster, although still poorly compared to human). Similarly disapponting was neural network use for character recognition, especially the for the handwriting. I think the key underplayed point in this article was: In benchmark testing using just a few spoken words, USC's Berger-Liaw Neural Network Speaker Independent Speech Recognition System not only bested all existing computer speech recognition systems but outperformed the keenest human ears. i.e. the phrase "few spoken words." That sheds the light how it was possible. The net had a handful of choices to decide among the words amid noise. While the researchers may have instructed humans to also watch for the same handful of words, thus appearing to even the conditions, the bulk of human recognition and perception occurs within deeply subconscious (automatic) feedback loops in the lower level sensory networks/filters. And these lower layers of sensory tuning are not tunable at will (consciously) but are tuned by the overall phoneme patterns a persons had learned. Thus even though a human may be instructed for the test that only 5 words may occur, human sensory perception will still be tuned for hundreds of thousands of patterns they have learned in their entire life. So this system seems to be one of those one trick dogs, outperforming humans in a very narrow task (after all, calculator can outperform everyone in arithmetic). Once the researchers move up to a system with comparable vocabulary to the human one, they'll run into the same problem everyone else did trying to teach neural nets to perform at the human level -- the performance doesn't scale up, it does well on a small problem, but as you scale the problem up, the network and the computation grow exponentially. In chess one could compare it to a computer being able to solve mates in few moves better than any human. But the search space grows exponentially with the length of the sequence and at some size it is beyond the simple search and requires more flexible strategies which are as yet not programmable.
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