الفهرس 
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Abstract
The thesis introduces a newly designed feature extraction method
for speech signal that can be used in Automatic Speech Recognition
(ASR). The newly developed methods are inherited from the
wavelet packets decomposition of speech signal. The work is an
enhancement of the original features developed earlier by other
researchers. Automatic Speech Recognition (ASR) of Arabic
Phones without Grammar is considered as a problem to be solved.
Information related to speech phoneme is encoded into 15 bits
instead of 7 bits in the original version of Best Tree Encoding
(BTE4). Best Tree Encoding of 5 levels of wavelet analysis (BTE5)
gives 25% efficiency enhancement over the original BTE4 for
solving ASR problem. Whenever possible the comparison results
are provided to explain the trend of enhancement in the Success
Rate (SR). In Addition; BTE5 achieved 25% SR while Mel-
Frequency-Cepstral-Coefficients (MFCC) achieved 39% Success
Rate of the ASR problem. This results in BTE5 achieving 64 % of
the SR of the popular and famous (MFCC) for the same ASR
problem