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|Title: ||Relative distance vector neural network (RDVNN) model: a hybrid approach to speech recognition|
|Authors: ||Elgasim, Elamin Elnima|
|Keywords: ||Relative Distance Vector Neural Network|
|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.|
|Appears in Collections:||Journal of the King Saud University - Computer & Information Sciences|
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