Development of a technical diagnostic method for voice quality impairments perceived in telephone communications, based on an analysis of speech signal

Les opérateurs de télécommunications se doivent de maîtriser et d'évaluer la qualité des services qu'ils offrent à leurs clients, dans un contexte en perpétuelle évolution. Comme alternative rapide et à moindre coût aux évaluations fondées sur l'interrogation d'utilisateurs, des outils de mesure ont été développés, qui intègrent des modèles permettant de prédire la qualité perçue. Cette thèse avait pour but de concevoir un outil de diagnostic de qualité vocale (applicable aux services de téléphonie), complémentaire à de tels modèles objectifs, afin d'obtenir des informations spécifiques sur la nature des défauts présents sur le signal audio et d'orienter vers des causes potentielles de ces défauts. En partant de l'hypothèse que la qualité vocale est multidimensionnelle, nous avons fondé l'outil de diagnostic sur la modélisation des quatre dimensions identifiées dans la littérature : la Bruyance, représentative des bruits de fond, la Continuité, relative à la perception des discontinuités dans le signal, la Coloration, liée aux distorsions du spectre de la voix, et la Sonie, traduisant la perception du niveau sonore. Chacune de ces dimensions est quantifiée à l'aide d'indicateurs de qualité issus de l'analyse du signal audio. Notre démarche a consisté, dans un premier temps, à rechercher dans des modèles objectifs récents (notamment la norme P.863 de l'UIT-T) des indicateurs de qualité et à en développer d'autres pour caractériser parfaitement chaque dimension. S'est ensuivie une étude de performances de ces indicateurs, les plus pertinents devant être intégrés dans notre outil de diagnostic. Finalement, pour chaque dimension, nous avons développé un module de classification automatique de défauts perçus en fonction de la nature du défaut identifié dans le signal, ainsi qu'un module supplémentaire estimant l'impact du défaut sur la qualité vocale. L'outil proposé couvre les trois bandes audio (bande étroite, bande élargie et bande super-élargie) couramment utilisées dans les systèmes de télécommunications avec, toutefois, une priorité pour les signaux en bande super-élargie, plus représentatifs des contenus audio qu'on sera amené à rencontrer dans les futurs services de télécommunications.Quality of service is a huge issue for telecommunications operators since they have to master and evaluate it in order to satisfy their customers. To replace expensive and time-consuming human judgment methods, objective methods, integrating objective models providing a prediction of the perceived quality, have been conceived. Our research aimed at developing a technical diagnostic method, complementary to objective voice quality models, which provides specific information about the nature of the perceived voice quality impairments and identifies the underlying technical causes. Assuming that speech quality is a multidimensional phenomenon, our technical diagnostic method is built on the modelling of the four perceptual dimensions identified in the literature: “Noisiness” relative to the perceived background noise, “Continuity” linked to discontinuity, “Coloration” related to frequency–response degradations and “Loudness” corresponding to the impact of the speech level, each one being quantified by quality degradation indicators based on audio signal analysis. A crucial step of our research was to find and/or to develop relevant quality degradation indicators to perfectly characterize each dimension. To do so, we identified quality degradation indicators in the most recent objective voice quality models (particularly the ITU-T P.863 recommendation, known as POLQA) and we analysed the performance of identified indicators. Then, the most relevant indicators have been considered in our diagnostic method. Finally, for each dimension, we proposed a detection block which automatically classifies a perceived degradation according to the nature of the defect detected in the audio signal, and an additional block providing information about the impact of degradations on speech quality. The proposed technical diagnostic method is designed to cover three bandwidths (Narrowband, Wideband and Super Wideband) used in telecommunications systems with a priority investigation to Super Wideband speech signals which remain very useful for future telephony applications

Performance evaluation of quality degradation indicators on super-wideband speech signals.

International audienceThis paper presents the performance of quality degradation indicators to be used in the context of super-wideband (50-14000 Hz) telephony. After an overview of these indicators, two analyses are undertaken: the first one considers conditions containing a single degradation and the second one considers conditions comprising several degradations at the same time, reflecting more realistic communications. This study highlights the major role of some indicators, and particularly those designed for quantifying the perceived additive noise, the frequency-response distortion and also the speech level. We show that these indicators are robust to multiple types of degradations and reveal relevant for advanced diagnosis of telecommunication systems

On the identification of relevant degradation indicators in super wideband listening quality assessment models

International audienceRecently, new objective speech quality evaluation methods, designed and adapted to new high voice quality contexts, have been developed. One interest of these methods is that they integrate voice quality perceptual dimensions reflecting the effects of frequency-response distortions, discontinuities, noise and/or speech level deviations respectively. This makes it possible to use these methods also to provide diagnostic information about specific aspects of the transmission systems' quality, as perceived by end-users. In this paper, we present and analyze in depth two of these approaches namely POLQA (Perceived Objective Listening Quality Assessment) and DIAL (Diagnostic Instrumental Assessment of Listening quality), in terms of quality degradation indicators related to the perceptual dimensions these models could embed. The main goal of our work is to find and propose the most robust quality degradation indicators to reliably characterize the impact of degradations relative to the perceptual dimensions described above and to identify the underlying technical causes in super wideband telephone communications [50, 14000] Hz. To do so, the first step of our study was to identify in both models the correspondence between perceptual dimensions and quality degradation indicators. Such indicators could be either present in the model itself or derived from our own investigation of the model. In a second step, we analyzed the performance and robustness of the identified quality degradation indicators on speech samples only impaired by one degradation (representative of one perceptual dimension) at a time. This study highlighted the reliability of some of the quality degradation indicators embedded in the two models under study and stood for a first step in the evaluation of performance of these indicators to quantify the degradation for which they were designed

Multi-outputs Gaussian process for predicting Burkina Faso COVID-19 spread using correlations from the weather parameters

The novel coronavirus has affected all regions of the world, but each country has experienced different rates of infection. In West Africa, in particular, infection rates remain low as compared to other parts of the world. This heterogeneity in the spread of COVID-19 raises a lot of questions that are still unanswered. However, some studies point out that people's mobility, size of gatherings, rate of testing, and weather have a great impact on the COVID-19 spread. In this work, we first evaluate the correlation between meteorological parameters and COVID-19 cases using Spearman's rank correlation. Secondly, multi-output Gaussian processes (MOGP) are used to predict the daily confirmed COVID-19 cases by exploring its relationships with meteorological parameters. The number of daily reported COVID-19 cases, as well as, weather variables collected from March 9, 2020, to October 18, 2021, were used in the analysis. The weather variables considered in the analysis are the mean temperature, relative humidity, wind direction, insolation, precipitation, and wind speed. The predicting model was constructed exploiting the correlation between the data of the daily confirmed COVID-19 cases and data of the weather variables. The results show that a significant correlation between the daily confirmed COVID-19 cases was found with humidity, wind direction, wind speed, and insolation. These parameters are used to construct the predictive model using the Multi-Output Gaussian process (MOGP). Different combinations of the data of meteorological parameters together with the data of daily reported COVID-19 cases were used to derive different models. We found that the best predictor is obtained using the combination of Humidity and insolation. This model is then used to predict the daily confirmed COVID-19 cases knowing the humidity and Insolation