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International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 6    
Website: http://www.ijser.org
scirp IJSER >> Volume 2, Issue 6, June 2011 Edition
Speech Recognition By Using Recurrent Neural Networks
Full Text(PDF, 3000)  PP.  
Author(s)
Dr.R.L.K.Venkateswarlu, Dr. R. Vasantha Kumari, G.Vani JayaSri
KEYWORDS
Frames, Mel-frequency cepstral coefficient, Multi Layer Perceptron (MLP), Neural Networks, Performance, Recurrent Neural Network (RNN), Utterances.
ABSTRACT
Automatic speech recognition by computers is a process where speech signals are automatically converted into the corresponding sequence of characters in text. In real life applications, however, speech recognizers are used in adverse environments. The recognition performance is typically degraded if the training and the testing environments are not the same. The study on speech recognition and understanding has been done for many years. The aim of the study was to observe the difference of English alphabet from E-set to AH-set. The aim of the study was to observe the difference of phonemes. Neural network is well-known as a technique that has the ability to classify nonlinear problem. Today, lots of researches have been done in applying Neural Network towards the solution of speech recognition. Even though positive results have been obtained from the continuous study, research on minimizing the error rate is still gaining lots of attention. This research utilizes Recurrent Neural Network, one of the Neural Network techniques to observe the difference of alphabet from E- set to AH - set. The purpose of this research is to upgrade the peoples knowledge and understanding on phonemes or word by using Recurrent Neural Network (RNN) and backpropagation through Multilayer Perceptron. 6 speakers (a mixture of male and female) are trained in quiet environment. The English language offers a number of challenges for speech recognition [4]. This paper specifies that the performance of Recurrent Neural Network is better than Multi Layer Perceptron Neural Network.
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