NESSR: a Neural Expert System for Speech Recognition

Artificial neural networks (ANNs) have found applications in large spectrum of fields. Satisfactory results are obtained particularly in classification problems. In speech recognition context, the use of ANNs is hard, this is essentially due to the absence of the temporal aspect in their structure. On the other hand, assuming a speech recognition task, a word could be recognized and well categorized or recognized and badly categorized ; so the explanation of the decision is very important. In this paper, we address two limitations of ANNs: the lack of explicit knowledge and the absence of temporal aspect in their implementation. STN: is a model of a specialized temporal neuron, which includes both symbolic and temporal aspects. To illustrate the STN utility, we consider a system for speech recognition ; we underline in this paper the explanation aspect of the system.Les réseaux de neurones ont été utilisés dans une large gamme d’applications. En particulier, des résultats satisfaisants ont été observés dans le domaine de la reconnaissance de formes. Toutefois, dans le contexte de la reconnaissance de la parole, l’utilisation des réseaux de neurones est difficile vue l’absence du paramètre temps dans leur structure. D’un autre point de vue, dans une application de reconnaissance de la parole, un mot peut être reconnu et bien classé ou reconnu et mal classé, il est donc impératif de pouvoir expliquer le raisonnement qui a conduit à cette décision. Dans cet article, nous considérons deux insuffisances des réseaux de neurones artificiels : le manque de connaissances explicites du domaine d’application et l’absence de l’aspect temps dans la structure des réseaux. À cet effet, nous proposons un modèle de neurone temporel et spécialisé, que nous appelons STN (pour specilized temporal neuron). Ce modèle est ensuite utilisé comme élément de base dans un réseau neuro-symbolique pour la reconnaissance de la parole

Text Extraction from Historical Document Images by the Combination of Several Thresholding Techniques

This paper presents a new technique for the binarization of historical document images characterized by deteriorations and damages making their automatic processing difficult at several levels. The proposed method is based on hybrid thresholding combining the advantages of global and local methods and on the mixture of several binarization techniques. Two stages have been included. In the first stage, global thresholding is applied on the entire image and two different thresholds are determined from which the most of image pixels are classified into foreground or background. In the second stage, the remaining pixels are assigned to foreground or background classes based on local analysis. In this stage, several local thresholding methods are combined and the final binary value of each remaining pixel is chosen as the most probable one. The proposed technique has been tested on a large collection of standard and synthetic documents and compared with well-known methods using standard measures and was shown to be more powerful

FEATURES AND NUMBER OF GAUSSIAN MIXTURES SELECTION FOR SINGING VOICE CLASSIFICATION IN COMMERCIAL MUSIC PRODUCTION

International audienceThe field of automatic classification of singing voices is still an open problem. In this context, we are interested with the classification and the assessment of singing voices. For this purpose, a sample database containing music files of Algerian singers is used. First, we make a separation between the voice and music parts in a song. Based on the vocal part, the some parameterswere extracted. This paper presents the parameters specifically designed for the analysis of a singing voice. A decision system was formed based on GMM (Gaussian mixture model); this system was used for classification of singing voice type and evaluation of singing voice quality. This paper presents our features selection task and the determination of the Gaussians number. Results show substantial improvements

Study of soil erosion risks using remote sensing in Ouergha River watershed (Morocco)

The watershed Ouergha River located in the north of Morocco suffer from vegetation cover degradation, this geographic entity is experiencing intense water erosion linked to the combination of several natural factors, such as the roughness and abundance of rainfall and the predominance of soft geological formations. Human intervention in this vulnerable environment accentuates its fragility by the clearing and degradation of the vegetation cover and the cultivation of land with a steep slope. This work aims to map the spatiotemporal evolution of this degradation by using the spot and Landsat images and the Radar image over a period from 1990 to 2014 data and aims to model its processes of erosion. In fact, the analysis of satellite data identified six main types of land use (eau, foret, reboisement…). It has also shown that the most degraded soils aren’t necessarily those with the greatest erosion rates over the past 15 years and that some soils that have developed well over time have become major exporters of sediments after clearing and cultivation. The comparison of the results of land use has highlighted the harmful impact of human practices on the acceleration of soil degradation. Human intervention, coupled to frequent and severe drought periods, remain the most important factors in the weakening and increasing vulnerability of soils to degradation. The results obtained by this approach made it possible to identify and monitor vulnerable areas at Ouergha watershed where interventions are needed to limit the processes of degradation of the soil and the natural environment

Geophysical prospecting in the Doukkala area (Swalah commune) in Morocco

The collapse of the subsoil creates a risk for the population whether it is urban or rural. Each year, the damage caused by these collapses has considerable socio-economic consequences, and the damage costs are very high. Thus, the detection of these areas of collapse in urban and rural areas is important to prevent and avoid socio-economic consequences, and to establish a preventive risk planning to have a better protection of people and goods. The commune of Swalah, study area, belongs to the province of El Jadida which is part of those areas of Doukkala exposed to the risk of collapse due to the presence of underground cavities. These cavities are potentially dangerous for humans, especially in urban areas. They have different extensions that can be caused by natural or anthropic origin. Their size, as well as the physical properties of the external environment in which they are located, allow the use of different geophysical methods. The use of these geophysical methods is the best to detect and delineate cavities in this region. The present study was based on a geophysical compaign of vertical electrical soundings. Indeed, 50 electrical soundings were modeled and reinterpreted and allowed to detect and delineate any potential cavities in the region