Voice recognition includes two basic parts: speech and speaker recognition. These
recognition processes consider as the most important processes of modern technologies,
many systems has been developed that differ in the methods used to extract feat
ures and
classification ways to support recognition systems of this type.
The study was conducted in this research on the previous subject, where the system
is designed to recognize the speaker and his voice orders and focus on several
complementary algorithms to carry out the research. we conducted an analytical study on
MFCC algorithm used in the extraction of features, and it has been studying two
parameters the number of filters in the filters bank and the number of features that taken
from each frame and the impact of these two parameters in the recognition rate and the
relationship of these two parameters on each other. It was the use of feed forwarding back
propagation neural networks performance analysis as characteristics and we analyze the
performance of the network to gain access to the best features and components to the
process of achieving recognition. And it has been studying Endpoint algorithm that used
to remove periods of silence and its impact on voice recognition rates.
