I experimented with Hopfield networks in the context of a voice-based, query-answering system. Hopfield networks are used to store and retrieve patterns. I used this technique to store queries represented as natural language sentences and I evaluated the accuracy of the technique for error correction in a spoken question-answering dialog between a computer and a user. I show that the use of an auto-associative Hopfield network helps make the speech recognition system more fault tolerant. I also looked at the available encoding schemes to convert a natural language sentence into a pattern of zeroes and ones that can be stored in the Hopfield network reliably, and I suggest scalable data representations which allow storing a large number of queries.
Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc5551 |
Date | 05 1900 |
Creators | Bireddy, Chakradhar |
Contributors | Tarau, Paul, Brazile, Robert |
Publisher | University of North Texas |
Source Sets | University of North Texas |
Language | English |
Detected Language | English |
Type | Thesis or Dissertation |
Format | Text |
Rights | Use restricted to UNT Community (strictly enforced), Copyright, Bireddy, Chakradhar, Copyright is held by the author, unless otherwise noted. All rights reserved. |
Page generated in 0.002 seconds