Assessing the Performance of a Speech Recognition System Embedded in Low-Cost Devices

Abstract

The main purpose of this research is to investigate how an Amazigh speech recognition system can be integrated into a low-cost minicomputer, specifically the Raspberry Pi, in order to improve the system's automatic speech recognition capabilities. The study focuses on optimizing system parameters to achieve a balance between performance and limited system resources. To achieve this, the system employs a combination of Hidden Markov Models (HMMs), Gaussian Mixture Models (GMMs), and Mel Frequency Spectral Coefficients (MFCCs) with a speaker-independent approach. The system has been developed to recognize 20 Amazigh words, comprising of 10 commands and the first ten Amazigh digits. The results indicate that the recognition rate achieved on the Raspberry Pi system is 89.16% using 3 HMMs, 16 GMMs, and 39 MFCC coefficients. These findings demonstrate that it is feasible to create effective embedded Amazigh speech recognition systems using a low-cost minicomputer such as the Raspberry Pi. Furthermore, Amazigh linguistic analysis has been implemented to ensure the accuracy of the designed embedded speech system.