Comparison of different multiclass SVM methods for speaker independent phoneme recognition

Abstract

Four multiclass Support Vector Machines (SVMs) methods were designed for the task of speaker independent phoneme recognition. These are the All-at-once, One-against-all, One-against-one, and the Directed Acyclic Graph SVM (DAGSVM). The Discrete Wavelet Transform (DWT) 8 frequency band power percentages are used for feature extraction. All tests were carried out on the TIMIT database. Comparable recognition rates were obtained from all designed systems. However, the One-against-One method performed best, achieving an accuracy of 53.70% for multi-speaker unlimited vocabulary speech. The phoneme recognition system, adopting the DWT and the One-against-one method, are intended to be implemented on a dedicated chip. The dedicated chip will improve the speed performance by approximately 100 times when comparing the hardware setup with the software implementation. This is obtained by providing the hardware parallelism, which accommodates the algorithms that have been used.