RAPID WEBGIS DEVELOPMENT FOR EMERGENCY MANAGEMENT

The use of spatial data during emergency response and management helps to make faster and better decisions. Moreover spatial data should be as much updated as possible and easy to access. To face the challenge of rapid and updated data sharing the most efficient solution is largely considered the use of internet where the field of web mapping is constantly evolving. ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action) is a non profit association founded by Politecnico di Torino and SITI (Higher Institute for the Environmental Systems) as a joint project with the WFP (World Food Programme). The collaboration with the WFP drives some projects related to Early Warning Systems (i.e. flood and drought monitoring) and Early Impact Systems (e.g. rapid mapping and assessment through remote sensing systems). The Web GIS team has built and is continuously improving a complex architecture based entirely on Open Source tools. This architecture is composed by three main areas: the database environment, the server side logic and the client side logic. Each of them is implemented respecting the MCV (Model Controller View) pattern which means the separation of the different logic layers (database interaction, business logic and presentation). The MCV architecture allows to easily and fast build a Web GIS application for data viewing and exploration. In case of emergency data publication can be performed almost immediately as soon as data production is completed. The server side system is based on Python language and Django web development framework, while the client side on OpenLayers, GeoExt and Ext.js that manage data retrieval and user interface. The MCV pattern applied to javascript allows to keep the interface generation and data retrieval logic separated from the general application configuration, thus the server side environment can take care of the generation of the configuration file. The web application building process is data driven and can be considered as a view of the current architecture composed by data and data interaction tools. Once completely automated, the Web GIS application building process can be performed directly by the final user, that can customize data layers and controls to interact with the

Compensation of Nuisance Factors for Speaker and Language Recognition

The variability of the channel and environment is one of the most important factors affecting the performance of text-independent speaker verification systems. The best techniques for channel compensation are model based. Most of them have been proposed for Gaussian mixture models, while in the feature domain blind channel compensation is usually performed. The aim of this work is to explore techniques that allow more accurate intersession compensation in the feature domain. Compensating the features rather than the models has the advantage that the transformed parameters can be used with models of a different nature and complexity and for different tasks. In this paper, we evaluate the effects of the compensation of the intersession variability obtained by means of the channel factors approach. In particular, we compare channel variability modeling in the usual Gaussian mixture model domain, and our proposed feature domain compensation technique. We show that the two approaches lead to similar results on the NIST 2005 Speaker Recognition Evaluation data with a reduced computation cost. We also report the results of a system, based on the intersession compensation technique in the feature space that was among the best participants in the NIST 2006 Speaker Recognition Evaluation. Moreover, we show how we obtained significant performance improvement in language recognition by estimating and compensating, in the feature domain, the distortions due to interspeaker variability within the same language. Index Terms—Factor anal