Abstract—Combined compression and classification problems are becoming increasingly important in many applications with large amounts of data and large sets of classes. This article presents the efficiency of ordered codebook learning vector quantization (OC-LVQ) for speech compression. The algorithm is based on competitive networks. It is developed and analyzed a learning vector quantization based algorithm for combined speech compression and classification. The Peak Signal to Noise Ratio (PSNR), Signal to Noise Ratio (SNR), and Normalized Root Mean Square Error (NEMSE) are used to measure the quality of speech signal. It provides the maximum quality at 28.9432 dB and 15.0333 dB for SNR and PSNR respectively. Also the minimum error of NEMSE is 0.1578.
Index Terms—Speech compression, ordered codebook, learning vector quantization.
The authors are with the Department of Electronic and Telecommunication, Rajamangala University of Technology Thanyaburi, Thailand (e-mail: firstname.lastname@example.org, email@example.com).
Cite: Kreangsak Pattanaburi and Jakkree Srinonchat, "Efficiency of Ordered Codebook Learning Vector Quantization for Speech Compression," International Journal of Information and Electronics Engineering vol. 2, no. 6, pp. 895-898, 2012.