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Impact of Languages and Accent on Perceived Speech Quality Predicted by Perceptual Evaluation of Speech Quality (PESQ) and Perceptual Objective Listening Quality Assessment (POLQA): Case of Moore, Dioula, French and English

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Open Journal of Applied Sciences 2021年第12期11卷 1324-1332页

作者： Daouda Konane Sibiri Tiemounou wend yam serge boris ouedraogo Laboratoire de Matériaux et Environnement Université Joseph KI-ZERBO Ouagadougou Burkina Faso Institut du Génie Informatique et Télécommunications Ecole Polytechnique de Ouagadougou Ouagadougou Burkina Faso

Perceptual Objective Listening Quality Assessment (POLQA) and Perceptual Evaluation of Speech Quality (PESQ) are commonly used objective standards for evaluating speech quality. These methods were developed and trained on native speakers’ speech sequences of some western languages. One can then wonder how these methods perform if they are applied to other languages or if the speaker is non-native. This paper deals with the evaluation of PESQ and POLQA on languages that were not been considered when setting up these methods, with emphasis on Moore and Dioula, two local languages of Burkina Faso. Another aspect is the evaluation of these two methods in the case of non-native speakers. For this purpose, in the one hand, the Mean Opinion Score-Listening Quality Objective (MOS-LQO) of PESQ and POLQA, computed for Moore and Dioula, are compared to those of French and English. On the second hand, the MOS-LQO scores of French and English are compared for native and non-native speakers, to evaluate the effect of the accent of speakers.

关键词： Speech Quality PESQ POLQA Language Accent

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An Exact Solution of Telegraph Equations for Voltage Monitoring of Electrical Transmission Line

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Energy and Power Engineering 2022年第11期14卷 669-679页

作者： Daouda Konane wend yam serge boris ouedraogo Toussaint Tilado Guingane Abdoulaye Zongo Zacharie Koalaga François Zougmoré Laboratoire de Matériauxet Environnement (LAME) Université Joseph KI-ZERBO Ouagadougou Burkina Faso Direction de la Distribution Société Nationale de l’Electricité du Burkina (SONABEL) Ouagadougou Burkina Faso

Telegraph equations are derived from the equations of transmission line theory. They describe the relationships between the currents and voltages on a portion of an electric line as a function of the linear constants of the conductor (resistance, conductance, inductance, capacitance). Their resolution makes it possible to determine the variation of the current and the voltage as a function of time at each point of the line. By adopting a general sinusoidal form, we propose a new exact solution to the telegraphers’ partial differential equations. Different simulations have been carried out considering the parameter of the 12/20 (24) kV Medium Voltage Cable NF C 33,220. The curves of the obtained solution better fit the real voltage curves observed in the electrical networks in operation.

关键词： Telegraph Equation Electric Network Voltage Variation Electrical Transmission Lines Analytical Solution

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Application of Machine Learning Methods on Climate Data and Commercial Microwave Link Attenuations for Estimating Meteorological Visibility in Dusty Condition

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Engineering（科研） 2022年第2期14卷 85-93页

作者： wend yam serge boris ouedraogo Sibiri Tiemounou Moumouni Djibo Ali Doumounia serge Roland Sanou Moumouni Sawadogo Idrissa Guira François Zougmore Laboratoire Matériaux et Environnement Université Joseph KI-ZERBO Ouagadougou Burkina Faso Institut du Génie Informatique et Télécommunications Ecole Polytechnique de Ouagadougou Ouagadougou Burkina Faso Ecole Normale Supérieure Koudougou Burkina Faso Autorité de Régulation des Communications électroniques et des Postes Ouagadougou Burkina Faso Telecel Faso SA Ouagadougou Burkina Faso

Accurately measuring meteorological visibility is an important factor in road, sea, rail, and air transportation safety, especially under visibility-reducing weather events. This paper deals with the application of Machine Learning methods to estimate meteorological visibility in dusty conditions, from the power levels of commercial microwave links and weather data including temperature, dew point, wind speed, wind direction, and atmospheric pressure. Three well-known Machine Learning methods are investigated: Decision Trees, Random Forest, and Support Vector Machines. The correlation coefficient and the mean square error, between the visibility distances estimated by Machine Learning methods and those provided by Burkina Faso weather services are computed. Except for the SVM method, all the other methods give a correlation coefficient greater than 0.90. The Random Forest method presents the best result both in terms of correlation coefficient (0.97) and means square error (0.60). For this last method, the best variables that explain the model are selected by evaluating the weight of each variable in the model. The best performance is obtained by considering the attenuation of the microwave signal and the dew point.

关键词： Meteorological Visibility Commercial Microwave Link Machine Learning Climate Parameters Dust Attenuation

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