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A hybrid neural network based speech recognition system for pervasive environments

Sehgal, Shoaib M.; Gondal, Iqbal and Dooley, Laurence S. (2004). A hybrid neural network based speech recognition system for pervasive environments. In: Proceedings of INMIC 2004. 8th International Multitopic Conference (INMIC'04), 24-26 Dec 2004, Lahore.

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One of the major drawbacks to using speech as the input to any pervasive environment is the requirement to balance accuracy with the high processing overheads involved. This paper presents an Arabic speech recognition system (called UbiqRec), which address this issue by providing a natural and intuitive way of communicating within ubiquitous environments, while balancing processing time, memory and recognition accuracy. A hybrid approach has been used which incorporates spectrographic information, singular value decomposition, concurrent self-organizing maps (CSOM) and pitch contours for Arabic phoneme recognition. The approach employs separate self-organizing maps (SOM) for each Arabic phoneme joined in parallel to form a CSOM. The performance results confirm that with suitable preprocessing of data, including extraction of distinct power spectral densities (PSD) and singular value decomposition, the training time for CSOM was reduced by 89%. The empirical results also proved that overall recognition accuracy did not fall below 91%.

Item Type: Conference or Workshop Item
Extra Information: ISBN: 0-7803-8680-9
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Item ID: 12146
Depositing User: Laurence Dooley
Date Deposited: 17 Nov 2008 08:36
Last Modified: 08 Dec 2018 00:21
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