DESIGN AND IMPLEMENTATION OF SPEECH RECOGNITION SYSTEM USING GENETIC ALGORITHM
Abstract
Speech recognition
technology is one from the fast growing engineering technologies. It has a
number of applications in different areas and provides potential benefits.
Nearly 20% people of the world are suffering from various disabilities; many of
them are blind or unable to use their hands effectively. The speech recognition
systems in those particular cases provide a significant help to them, so that
they can share information with people by operating computer through voice
input. This project is designed and developed keeping that
factor into mind, and a little effort is made to achieve this aim. Our project
is capable to recognize the speech and convert the input audio into text; it
also enables a user to perform operations such as “save,
open, exit” a file by providing voice input. It also helps the user to open
different system software such as opening Ms-paint, notepad and calculator. At the initial level
effort is made to provide help for basic operations as discussed above, but the
software can further be updated and enhanced in order to cover more operations.
BACKGROUND OF THE STUDY
INTRODUCTION
The development for speech recognition
system has been for a while. The recognition platform can be divided into three
types. Dynamic Time Warping (DTW) (SAKOE, 1978), the earliest platform, uses the
variation in frame's time for adjustment and further recognition. Later,
Artificial Neural Network (ANN) replaced DTW. Finally, Hidden Markov Model was
developed to adopt statistics for improved recognition performance. Besides the recognition platform, the
process of speech recognition also includes: recording of voice signal, point
detect, pre-emphasis, speech feature capture, etc. The final step is to
transfer the input sampling feature to recognition platform for matching. In recent years, study on Genetic
Algorithm can be found in many research papers (Chu, 2003a; Chen, 2003; Chu,
2003b). They demonstrated different characteristics in Genetic Algorithm than
others. For example, parallel search based on random multi-points, instead of a
single point, was adopted to avoid being limited to local optimum. In the
operation of Genetic Algorithm, it only needs to establish the objective
function without auxiliary operations, such as differential operation.
Therefore, it can be used for the objective functions for all types of
problems.
Because artificial neural network has
better speech recognition speed and less calculation load than others, it is
suitable for chips with lower computing capability. Therefore, artificial
neural network was adopted in this study as speech recognition platform. Most
artificial neural networks for speech recognition are back-propagation neural
networks. The local optimum problem (Yeh, 1993) with Steepest Descent Method
makes it fail to reach the highest recognition rate. In this study, Genetic
Algorithm was used to improve the drawback. Consequently, the mission of this chapter
is the experiment of speech recognition under the recognition structure of
Artificial Neural Network (ANN) which is trained by the Genetic Algorithm (GA).
This chapter adopted Artificial Neural Network (ANN) to recognize Mandarin
digit speech. Genetic algorithm (GA) was used to complement Steepest Descent
Method (SDM) and make a global search of optimal weight in neural network.
Thus, the performance of speech recognition was improved. The nonspecific
speaker speech recognition was the target of this chapter. The experiment in
this chapter would show that the GA can achieve near the global optimum search
and a higher recognition rate would be obtained. Moreover, two method of the
computation of the characteristic value were compared for the speech
recognition.
However, the drawback of GA used to train
the ANN is that it will waste many training time. This is becasue that the numbers
of input layer and output layer is very large when the ANN is used in
recognizing speech. Hence, the parameters in the ANN is emormously increasing.
Consequently, the training rate of the ANN becomes very slow. It is then
necessary that other improved methods must be investigated in the future
research.
The rest of this chapter is organized as
follows. In section 2, the speech pre-processing is introduced. Then, in
section 3 we investigate the speech recognition by ANN which is trained by
genetic algorithm to attain global optimal weights.
1.1
Project Objective
•
To
understand the speech recognition and its fundamentals.
•
Its
working and applications in different areas
•
Its
implementation as a desktop Application
•
Development
for software that can mainly be used for:
•
Speech
Recognition
•
Speech
Generation
•
Text
Editing
•
Tool
for operating Machine through voice.
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