Hardware-based support vector machine for phoneme classification

Abstract

This paper presents the design of a digital hardware implementation based on Support Vector Machines (SVMs), for the task of multi-speaker phoneme recognition. The One-against-one multiclass SVM method, with the Radial Basis Function (RBF) kernel was considered. Furthermore, a priority scheme was also included in the architecture, in order to forecast the three most likely phonemes. The designed system was synthesised on a Xilinx Virtex-II XC2V3000 FPGA, and evaluated with the TIMIT corpus. This phoneme recognition system is intended to be implemented on a dedicated chip, along with the Discrete Wavelet Transforms (DWTs) for feature extraction, to further improve the resultant performance.