Search

In this study, the authors explore the integration of speaker-independent automatic Amazigh speech recognition technology into interactive applications to extract data remotely from a distance database. Based on the combined interactive voice response (IVR) and automatic speech recognition (ASR) technologies, the authors built an interactive speech system to allow users to interact with the interactive system through voice commands. The hidden Markov models (HMMs), Gaussian mixture models (GMMs), and Mel frequency spectral coefficients (MFCCs) are used to develop a speech system based on the ten first Amazigh digits and six Amazigh words. The best-obtained performance is 89.64% by using 3 HMMs and 16 GMMs.

This paper is a part of our contributions to research on the ongoing COVID-19 pandemic around the world. This research aims to use Hidden Markov Model (HMM) based automatic speech recognition system to analyze the cough signal and determine whether the signal belongs to a sick or healthy speaker. We built a configurable model by using HMMs, Gaussian Mixture Models (GMMs), Mel frequency spectral coefficients (MFCCs) and a cough corpus collected from healthy and sick voluntary speakers. Our proposed method is able to classify dry cough with sensitivity from 85.86% to 91.57%, differentiate the dry cough, and cough COVID-19 symptom with specificity from 5 to 10%. The obtained results are very encouraging to enrich our corpus with more data and increase the performance of our diagnostic system.

As part of our contributions to researches on the ongoing COVID-19 pandemic worldwide, we have studied the cough changes to the infected people based on the Hidden Markov Model (HMM) speech recognition classification, formants frequency and pitch analysis. In this paper, An HMM-based cough recognition system was implemented with 5 HMM states, 8 Gaussian Mixture Distributions (GMMs) and 13 dimensions of the basic Mel-Frequency Cepstral Coefficients (MFCC) with 39 dimensions of the overall feature vector. A comparison between formants frequency and pitch extracted values is realized based on the cough of COVID-19 infected people and healthy ones to confirm our cough recognition system results. The experimental results present that the difference between the recognition rates of infected and non-infected people is 6.7%. Whereas, the formant analysis variation based on the cough of infected and non-infected people is clearly observed with F1, F3, and F4 and lower for F0 and F2.

This paper investigates the Amazigh speech recognition and its usage for controlling external devices. We describe our experience to design a speech system founded on hidden Markov Models (HMMs), Gaussian mixture models (GMMs), Mel frequency spectral coefficients (MFCCs) and optimization of parameters in order to have a portability in resource limited embedded system. Our objective is developing a control Amazigh speech recognition system through a Raspberry Pi board, as well as achieving the best solution with a higher automatic speech recognition parametrization for lowcost minicomputers on a speaker-independent approach. The designed speech system was implemented on the open-source platform. The system achieves the best performance of 90.43% when trained by using 3 HMMs and 16 GMMs.

Speech recognition is a method that allows machines to convert the incoming speech signals into text commands. This paper presents a brief survey on automatic speech recognition systems based on HTK, Julius, MATLAB, Sphinx and Kaldi. A description of the mentioned speech recognition systems is discussed, and the structure and performance of these different systems are presented.

This paper aims to build an interactive speaker-independent automatic Amazigh speech recognition system. The proposed system offers a methodology to extract data remotely from a distance database using the combined interactive voice response (IVR) and automatic speech recognition (ASR) technologies. We describe our experience to design an interactive speech system based on hidden Markov models (HMMs), Gaussian mixture models (GMMs) and Mel frequency spectral coefficients (MFCCs) based on ten first Amazigh digits and six Amazigh words. The best-obtained performance is 89.64% by using 3 HMMs and 16 GMMs.

Automatic speech recognition (ASR) in Amazigh speech, particularly Moroccan Tarifit accented speech, is a less researched area. Some efforts to develop ASR on Moroccan accented Amazigh speech in the clean environment have been studied in our previous works. In this paper, we analyze the effect of noise car at different signal-to-noise ratio (SNR) on Amazigh accented on the first ten digits with different decibel values. Various techniques are used for isolated speech recognition like hidden Markov model (HMMs) and Mel-frequency cepstral coefficients (MFCC). The experimental result presents that recognition rate of 88.22% in a clean environment, and 59.26% and 33.83% rates in noisy condition at SNR 10 dB and 20 dB, respectively.