Efficient robust speech recognition with empirical mode decomposition using an FPGA chip with dual core - Shing-Tai Pan; Ching-Fa Chen; Wen-Sin Tseng (International Journal of Reconfigurable and Embedded Systems (IJRES), 2020)
The purpose of this paper is to accelate the computing speed of Empirical Mode Decomposition (EMD) based on multi-core embedded systems for robust speech recognition. A reconfigurable chip, Field Programmable Gate Array (FPGA), is used for the implementation of the designed system. This paper applies EMD to discompose some noised speech signals into several Intrinsic Mode Functions (IMFs). These IMFs will be combined to recover the original speech by multiplying their corresponding weights which were trained by Genetic Algorithms (GA). After applying Empirical Mode Decomposition (EMD), we obtain a cleaner speech for recognition. Due to the complexity of the computation of the EMD, a dual-core architecture of embedded system on FPGA is proposed to accelerate the computing speed of EMD for robust speech recognition. This will enhance the efficiency of embedded speech recognition system.