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Abstract
This thesis introduces two different techniques for Arabic speech compression. These techniques are Gaussian Mixture Model (GMM) and Wavelet Transform (WT) techniques and a comparison between these techniques is given. It is found that: Supervised learning technique is used to evaluate the PDF parameters. Gaussian Mixture Model (GMM) based on EM algorithm gives good compression ratios with acceptable SNR. For mono and stereo voice, the quality of output speech is nearly identical.
• Wavelet transform can be used for speech signals. This is because it analyzes the speech both in time and frequency domains. Wavelet transform (WT) can be used for speech compression based on threshold value, as it gives good compression ratios.
• The Bior3.1 and Db10 wavelet functions can be used for speech compression as they give better results than other wavelet functions.
• Increasing the level of decomposition increases the compression ratio, but after a certain level, the compression ratio becomes approximately constant. No further enhancements were achieved beyond level 2 decomposition.
• For both Gaussian Mixture Model (GMM) and Wavelet transform (WT), the signal to noise ratio is decreased when compression ratio increases.
• Gaussian Mixture Model is better than Wavelet transform in speech compression especially for Holy Quran even though for high compression ratios.