ATVS-UAM NIST LRE 2009 System Description

Official contribution of the National Institute of Standards and Technology; not subject to copyright in the United States.ATVS-UAM submits a fast, light and efficient single system. The use of a task-adapted nonspeech-recognition-based VAD (apart from NIST conversation labels) and gender-dependent total variability compensation technology allows our submitted system to obtain excellent development results with SRE08 data with exceptional computational efficiency. In order to test the VAD influence in the evaluation results, a contrastive equivalent system has been submitted exclusively changing ATVS VAD labels with BUT publicly contributed ones. In all contributed systems, two gender-independent calibrations have been trained with respectively telephone-only and mic (either mic-tel, tel-mic or mic-mic) data. The submitted systems have been designed for English speech in an application-independent way, all results being interpretable in the form of calibrated likelihood ratios to be properly evaluated with Cllr. Sample development results with English SRE08 data are 0.53% (male) and 1.11% (female) EER in tel-tel data (optimistic as all English speakers in SRE08 are included in total variability matrices), going up to 3.5% (tel-tel) to 5.1% EER (tel-mic) in pessimistic cross-validation experiments (25% of test speakers totally excluded from development data in each xval set). The submitted system is extremely light in computational resources, running 77 times faster than real time. Moreover, once VAD and feature extraction are performed (the heaviest components of our system), training and testing are performed respectively at 5300 and 2950 times faster than real time

ATVS-UAM ALBAYZIN-VL08 System description

Actas de las V Jornadas en Tecnología del Habla (JTH 2008)ATVS submission to ALBAYZIN-VL08 will consist of different combinations of a set of acoustic and phonotactic subsystems that our group has developed during the last years. Most of these subsystems have already been evaluated on NIST LRE 07 evaluation. At the time of writing this system description some of the details of our submission are still undefined. Therefore we will briefly describe our systems and the intended combinations to be submitted, but these settings should not be taken as final in any way. As acoustic subsystems we will use a GMM SuperVectors and a GLDSSVM subsystem, while the phonotactic subsystem will be a PhoneSVM system. We are still deciding the best fusion strategy and the best combination of subsystems at the time of writing. Output scores will be submitted in the form of loglikelihood ratio (logLR) scores in an application independent way. Open-set detection thresholds will be set to the Bayes thresholds in all cases, and the same logLR sets will probably be submitted to the closed- and open-set conditions.This work was funded by the Spanish Ministry of Science and Technology under project TEC2006-13170-C02-01

Multilevel and session variability compensated language recognition: ATVS-UAM systems at NIST LRE 2009

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. J. Gonzalez-Dominguez, I. Lopez-Moreno, J. Franco-Pedroso, D. Ramos, D. T. Toledano, and J. Gonzalez-Rodriguez, "Multilevel and Session Variability Compensated Language Recognition: ATVS-UAM Systems at NIST LRE 2009" IEEE Journal of Selected Topics in Signal Processing, vol. 4, no. 6, pp. 1084 – 1093, December 2010This work presents the systems submitted by the ATVS Biometric Recognition Group to the 2009 Language Recognition Evaluation (LRE’09), organized by NIST. New challenges included in this LRE edition can be summarized by three main differences with respect to past evaluations. Firstly, the number of languages to be recognized expanded to 23 languages from 14 in 2007, and 7 in 2005. Secondly, the data variability has been increased by including telephone speech excerpts extracted from Voice of America (VOA) radio broadcasts through Internet in addition to Conversational Telephone Speech (CTS). The third difference was the volume of data, involving in this evaluation up to 2 terabytes of speech data for development, which is an order of magnitude greater than past evaluations. LRE’09 thus required participants to develop robust systems able not only to successfully face the session variability problem but also to do it with reasonable computational resources. ATVS participation consisted of state-of-the-art acoustic and high-level systems focussing on these issues. Furthermore, the problem of finding a proper combination and calibration of the information obtained at different levels of the speech signal was widely explored in this submission. In this work, two original contributions were developed. The first contribution was applying a session variability compensation scheme based on Factor Analysis (FA) within the statistics domain into a SVM-supervector (SVM-SV) approach. The second contribution was the employment of a novel backend based on anchor models in order to fuse individual systems prior to one-vs-all calibration via logistic regression. Results both in development and evaluation corpora show the robustness and excellent performance of the submitted systems, exemplified by our system ranked 2nd in the 30 second open-set condition, with remarkably scarce computational resources.This work has been supported by the Spanish Ministry of Education under project TEC2006-13170-C02-01. Javier Gonzalez-Dominguez also thanks Spanish Ministry of Education for supporting his doctoral research under project TEC2006-13141-C03-03. Special thanks are given to Dr. David Van Leeuwen from TNO Human Factors (Utrech, The Netherlands) for his strong collaboration, valuable discussions and ideas. Also, authors thank to Dr. Patrick Lucey for his final support on (non-target) Australian English review of the manuscript

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Who am I talking with? A face memory for social robots

In order to provide personalized services and to develop human-like interaction capabilities robots need to rec- ognize their human partner. Face recognition has been studied in the past decade exhaustively in the context of security systems and with significant progress on huge datasets. However, these capabilities are not in focus when it comes to social interaction situations. Humans are able to remember people seen for a short moment in time and apply this knowledge directly in their engagement in conversation. In order to equip a robot with capabilities to recall human interlocutors and to provide user- aware services, we adopt human-human interaction schemes to propose a face memory on the basis of active appearance models integrated with the active memory architecture. This paper presents the concept of the interactive face memory, the applied recognition algorithms, and their embedding into the robot’s system architecture. Performance measures are discussed for general face databases as well as scenario-specific datasets

Automating Metadata Extraction: Genre Classification

A problem that frequently arises in the management and integration of scientific data is the lack of context and semantics that would link data encoded in disparate ways. To bridge the discrepancy, it often helps to mine scientific texts to aid the understanding of the database. Mining relevant text can be significantly aided by the availability of descriptive and semantic metadata. The Digital Curation Centre (DCC) has undertaken research to automate the extraction of metadata from documents in PDF([22]). Documents may include scientific journal papers, lab notes or even emails. We suggest genre classification as a first step toward automating metadata extraction. The classification method will be built on looking at the documents from five directions; as an object of specific visual format, a layout of strings with characteristic grammar, an object with stylo-metric signatures, an object with meaning and purpose, and an object linked to previously classified objects and external sources. Some results of experiments in relation to the first two directions are described here; they are meant to be indicative of the promise underlying this multi-faceted approach.