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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/3293

Title: Relative distance vector neural network (RDVNN) model: a hybrid approach to speech recognition
Authors: Elgasim, Elamin Elnima
Keywords: Relative Distance Vector Neural Network
Hybrid Approach
Speech Recognition
Computer Sciences
Issue Date: 2004
Publisher: King Saud University
Citation: Journal of King Saud University, Computer and Information Science: 17; 1-21
Abstract: This paper introduces a novel insight to the problem of Automatic Speech Recognition (ASR). Worldwide many practical systems had been developed for ASR. Most of these systems were based on Hidden Markov Models (HMM). This is state-of-the-art paradigm in ASR. Despite the fact that HMMs are successful under a diversity of conditions, they do suffer from some limitations that limit their applicability to real-world noisy environments. As a result, several researchers moved to Artificial Neural Networks (ANNs) as an alternative technique for ASR, in order to overcome the limitations encountered in pure HMM implementation. Soon after, interest moved over to hybrid systems that combine HMMs and ANNs within a single unifying hybrid architecture. In this study a hybrid DTW/ANN ASR system will be introduced, explained, implemented and analyzed, which has been given the name Relative Distance Vector Neural Network (RDVNN) Model. Adequate experiments had been performed to reveal the main characteristics of the present novel hybrid ASR system. The results are believed to be encouraging and the system is easy to implement. For speaker dependent the accuracy is near perfect (error rate is less than 1%). For speaker independent models the results attained are comparable with most world-wide results known for the state-of-the-art ASR small task systems. Many aspects of the RDVNN technique are illustrated through experimental work to demonstrate these findings. One of the main advantages of the RDVNN method is that it can be applied to various other similar problem domains.
URI: http://hdl.handle.net/123456789/3293
Appears in Collections:Journal of the King Saud University - Computer & Information Sciences

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