A NOVEL SPEECH COMPRESSION TECHNIQUE USING OPTIMIZED WAVELET TRANSFORM TO IMPROVE THE QUALITY OF AUDITORY PERCEPTION UNDER LOW SNR CONDITIONS

摘要

Speech compression in poor environment, where the signal energy is weak due to acoustical disturbances, can improvethe efficiency of transmission while reducing the bandwidth if intelligibility and/or quality can be preserved by selectingappropriate energy based wavelets. We propose an Optimized Wavelet Transform (OWT) to improve speech perception byincorporating masking techniques in the algorithm to reduce the noise effect. Adaptive Wavelet Selection followed by optimizedquantization exploit a robust Dynamic Dictionary Scheme (DDS) to perform efficient compression while preserving speechintelligibility and perceptual quality. An additional lossless coding technique inevitably increases the compression ratio whilepreserving the quality of the signal. Finally, decompressing the compressed signal undergoes tonal and noise masking by applying aglobal threshold based on Sub-Band Perceptual Factor (SBPF) and Perceptual Entropy (PE), which improves the quality of thesignal. Performance of the proposed algorithm is obtained in terms of Normalized Root-Mean Square Error (NRMSE),Compression Ratio (CR), Performance Evaluation of Speech Quality (PESQ), Re-construction Distortion Length (RDL), Signal toNoise Ratio (SNR) for various voiced and unvoiced signals recorded in low SNR conditions. All the signals are derived fromNOIZEUS data base and some samples are recorded and normalized to operate at sampling frequency of 8KHz.