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

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\u27s 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

Speech Recognition Algorithms based Cough Recognition System

This paper introduces an innovative technique for creating a cough detection system that relies on speech recognition algorithms. The strategy utilizes the Kaldi platform, which is open source and incorporates a hybrid system of Gaussian Mixture Model-based Hidden Markov Models (GMM-HMM) through a straightforward monophone training model. Additionally, the study examines the effectiveness of two different feature extraction approaches, Mel Frequency Cepstral Coefficient (MFCC) and Perceptual Linear Prediction (PLP). The proposed system can function as a collection tool for gathering natural and spontaneous cough data from conversations or continuous speech. The paper also compares the Kaldi and CMU Sphinx4 toolkits, concluding that Kaldi’s use of GMM-HMM outperforms CMU Sphinx4