FPGA Implementation of Support Vector Machine Based Isolated Digit Recognition System

In this paper, two schemes for FPGA implementation of multi-class SVM based isolated digit recognition system are proposed, one using only logic elements and another using both soft-core processor and logic elements(LEs). One of the major contributions of this paper is the proposal for implementation of the decision function using only fixed point arithmetic without compromising the recognition accuracy. Compared to the scheme which uses floating point arithmetic, the proposed scheme reduces the number of LEs required by a factor of 3.29. The second scheme proposed results in about 25 times lower area compared to the first scheme. For the soft-core processor approach, a custom instruction is proposed for floating point arithmetic. Speaker dependent TI46 database of isolated digits is used for training and testing. Features are extracted using both Linear Predictive Coefficients (LPC) and Mel Frequency Cepstral Coefficients(MFCC) and features are compressed using Self Organized Feature Mapping (SOFM). This in turn is used by the SVM classifier to evaluate the recognition accuracy and the hardware resources utilized. Both the schemes proposed result in 100% recognition accuracy when implemented on Altera Cyclone II FPGA. The proposed schemes can also be used for speaker verification and speaker authentication applications. Since the scheme which uses soft-core processor requires lower area, it can be used for systems which require a large vocabulary size.