الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
Speech signals are the language of communication among people anywhere in
the world. To transmit these signals through the channel, they need a large number of
bits. Thus, they require large channel bandwidth. Therefore, speech coding and
compression are considered as the solution to this problem. Speech coding has a vital
role in the speech processing area. Speech coding converts the analog speech signal
into compressed binary form. The goal of the conversion process is to reduce the
number of bits needed for transmission. Thus, the cost is decreased. This thesis is
concerned with efficient coding and compression of speech signals. In addition, the
effect of decoded and decompressed speech signals on the Speaker Identification (SI)
for remote access systems is studied.
In this thesis, speech coding and two compression techniques are used. The
applied speech coding technique is the Linear Predictive Coding (LPC) as it is the
most popular technique in mobile communications. The first compression technique
for speech signals depends on the decimation process for compression, and thus the
original speech signal is reconstructed using inverse techniques. Inverse techniques
include maximum entropy and regularized solutions. On the other hand, the second
compression technique is Compressed Sensing (CS). The coding and compression
techniques are compared and the performance of the recovered signal is evaluated
using two metrics; the Perceptual Evaluation of Speech Quality (PESQ), Dynamic
Time Warping (DTW). The results prove that the CS technique works efficiently in
the absence and in the presence of noise.
Also, in this thesis the effect of decoded or decompressed speech signals on
the performance of SI system in the remote access scenario is investigated. For the SI
system, feature vectors are captured from different discrete transforms such as
Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Discrete
Sine Transform (DST), in addition to the features from the time domain. Finally, a
comparison between the effects of all speech communication scenarios on the SI
system is presented. Simulation results prove the success of speaker identification
process even in the presence of reconstruction loss and channel effect.