Enhancement of Compressed Speech Signal using Recursive Filter

Abstract

Speech compression, enhancement and recognition in noisy, reverberant conditions is a challenging task. In this paper a new approach to this problem, which is developed in the framework of probabilistic random modeling. speech coding techniques are commonly used in low bit rate analysis and synthesis . 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. As the compression techniques that are used are Lossy compression technique and there is every possibility of loss in quality. Speech enhancement aims to improve speech quality by using various algorithms. This paper deals with multistage vector quantization technique used for coding (compression) 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 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. The existing Speech enhancement techniques like spectral subtraction and Kalman filters performances are compared with the proposed recursive filter and approach yields significantly estimating the parameters like signal to noise ratio subjected to white Gaussian Noise and Real time noise signals.