Compression of Speech Signals using MSVQ and its Enhancement Using Spectral Subtraction & Kalman filter and its performance comparisions

Coding algorithms seek to minimize the bit rate in the digital representation of a signal without an objectionable loss of signal quality in the process. Speech enhancement means improvement in intelligibility and/or quality of a speech signal. This paper deals with multistage vector quantization technique used for coding of narrow band speech signals. The parameter used for coding of speech signals are the line spectral frequencies, so as to ensure filter stability after quantization. The code books used for quantization are generated by using Linde, Buzo and Gray(LBG) algorithm. The results of the multistage vector quantizer are compared with unconstrained vector quantization Technique. The performance of quantization is measured in terms of spectral distortion measured in dB, Computational complexity measured in KFlops and Memory Requirements measured in Floats. From the results it can be proved that multistage vector quantization is having better spectral distortion performance, less computational complexity and memory requirements when compared to unconstrained vector quantization. Speech enhancement is a special case of signal estimation as speech is non stationary and hence human ear is the final judge and we does not believe in mathematical error criterion.The compressed speech signals are enhanced using spectral subtraction and Kalman Filter the outputs are compared with original signals with 0dB & 10dB noise.