Comparative analysis of compression techniques for Tamil speech datasets

Abstract

Speech compression plays a significant role in speech communication systems. It yields a compact data representation allowing an efficient storage and transmission of information. The potentiality of speech compression has been essential to the technologies of robust long-distance communication, high-quality speech storage, and message encryption. Storage and archival of large volumes of spoken information makes speech compression essential and which improves the capacity of communications relatively of unlimited bandwidth. The main objective of this research work is to present a good quality speech data at a low bit rate. In order to accomplish this, the most powerful speech analysis and compression techniques such as Linear Predictive Coding (LPC), Discrete Cosine Transformation (DCT) and Discrete Wavelet Transformation (DWT) are adopted for Tamil speech database. The adopted techniques are evaluated based on Compression ratio, Peak Signal to Noise Ratio (PSNR), Normalized Root Mean Square Error (NRMSE) and Mean Opinion Score (MOS). Among these methods the DWT achieves greater performance than other two techniques employed in this research work.