PSoC based isolated speech recognition system

Abstract

Isolated Speech recognition systems (ISRS) have been implemented using microprocessors, digital signal processors and FPGAs and have been reported in the literature. In this paper, the study and implementation of an ISRS using Cypress Programmable System on Chip (PSoC) is presented. For the implementation, PSoC5 containing the ARM Cortex-M3 CPU is used. Recognition performance is studied using three feature extraction techniques (Zero crossing, Zero crossing with end point detection and Zero crossing with end point detection and Cochlear filter) and minimum distance classifier. The ISRS is trained and tested using 10 utterances of three words. The performance is evaluated for utterances by both single speaker and multiple speaker. From this study, it is found that the system using the Zero crossing with end point detection and Cochlear filter gives the highest recognition accuracy of 80%and 70% respectively for the utterances by single speaker and multiple speakers. Higher recognition accuracy can be obtained using more robust feature extraction and classification techniques at the cost of increase in the computation complexity and storage space. The proposed ISRS has applications in voice operated robots, appliances and access control systems.