Carmen García-Mateo

The Multiscenario Multienvironment BioSecure Multimodal Database (BMDB)

A new multimodal biometric database designed and acquired within the framework of the European BioSecure Network of Excellence is presented. It is comprised of more than 600 individuals acquired simultaneously in three scenarios: 1 over the Internet, 2 in an office environment with desktop PC, and 3 in indoor/outdoor environments with mobile portable hardware. The three scenarios include a common part of audio/video data. Also, signature and fingerprint data have been acquired both with desktop PC and mobile portable hardware. Additionally, hand and iris data were acquired in the second scenario using desktop PC. Acquisition has been conducted by 11 European institutions. Additional features of the BioSecure Multimodal Database (BMDB) are: two acquisition sessions, several sensors in certain modalities, balanced gender and age distributions, multimodal realistic scenarios with simple and quick tasks per modality, cross-European diversity, availability of demographic data, and compatibility with other multimodal databases. The novel acquisition conditions of the BMDB allow us to perform new challenging research and evaluation of either monomodal or multimodal biometric systems, as in the recent BioSecure Multimodal Evaluation campaign. A description of this campaign including baseline results of individual modalities from the new database is also given. The database is expected to be available for research purposes through the BioSecure Association during 2008.

On combining classifiers for speaker authentication

Abstract Speaker verification and utterance verification are examples of techniques that can be used for speaker authentication purposes. Speaker verification consists of accepting or rejecting the claimed identity of a speaker by processing samples of his/her voice. Usually, these systems are based on HMM models that try to represent the characteristics of the speakers’ vocal tracts. Utterance verification systems make use of a set of speaker-independent speech models to recognize a certain utterance. If the utterances consist of passwords, this can be used for identity verification purposes. Up to now, both techniques have been used separately. This paper is focused on the problem of how to combine these two sources of information. New architectures are presented to join an utterance verification system and a speaker verification system in order to improve the performance in a speaker verification task.

Fast LM look-ahead for large vocabulary continuous speech recognition using perfect hashing

In this paper we present a fast method to implement a language model (LM) look-ahead algorithm in a Viterbi-based, single-lexical-tree speech recognizer. We have used three different mechanisms to speed up the calculation: a cache memory attached to each node or the network, a pre-calculation of the probabilities of the active contexts, and an organization of the LM using perfect hash. These enhancements make it possible to use the full trigram LM to compute the look-ahead with better overall results, both in terms of recognition rate and computation time, than using a unigram or bigram based approximation.

Soft decoding strategies for distributed speech recognition over IP networks

In distributed speech recognition, speech feature vectors are obtained at the client side, and transmitted to the remote server for recognition. In this paper, we investigate the robustness of the remote recognizer against the inherent packet loss in an Internet communication. In the decoding process, we propose to apply techniques used for missing data problems. The idea is to use a simple approach of error concealment to recover the non-received speech frames, and then to consider these recovered speech frames as not completely reliable. Thus, at recognition stage, our recognition engine uses a weighted (or soft decision) Viterbi algorithm in order to take into account the reliability of the recovered speech frames. Results on Aurora databases show that the proposed approach provides good recognition performance over a wide range of network conditions.

Performance analysis of Distributed Speech Recognition over IP networks on the AURORA database

We present results on the performance of Distributed Speech Recognition operating over simulated IP networks. ETSI AURORA front-end running at client nodes extracts the speech parameters, packetizes and sends them as real-time IP traffic to a remote recognizer based on Continuous Density Hidden Markov Models. The experimental framework is the ETSI STQ-AURORA Project Database 2.0. The impact of transmission over IP networks is modeled by (1) random losses, (2) losses generated by a Gilbert model and (3) network simulations. Results show that random losses and moderately bursty losses do not significantly affect the recognition performance. Strongly bursty packet losses, as those generated by real-time and Web traffic competing over a network bottleneck, instead, can have a very negative impact on recognition performance, indicating that DSR over the Internet, to be successful, requires high levels of Quality of Service.

Pose-corrected face processing on video sequences for webcam-based remote biometric authentication

We describe a framework aimed at performing facebased nbiometric user authentication for Web resources through nclient–server secured sessions. A novel front end for face video sequences nprocessing is developed in which face detection and shot nselection are performed at the client side while statistical multishot npose-corrected face verification is performed at the server side. We nexplain all the image processing steps, from acquisition through decision, npaying special attention to a PDM-based pose correction nsubsystem and a GMM-based sequence decision test. The pose ncorrection relies on projecting a face-shape mesh onto the set of nPDM eigenvectors and back-projecting it after changing the coefficients nassociated with pose variation. The aligned texture features ncompose the observation vectors ready to be plugged into a GMMbased nlikelihood ratio for statistical decision. The pose correction nstrategy has been previously validated over the XM2VTS and CMUPIE ndata sets, the GMM-based video sequence decision test has nbeen compared to other video-based systems using the BANCA ndata set, and the complete proposed system has been tested on a nnew video data set from the European Network of Excellence nBIOSECURE.

An open framework for distributed biometric authentication in a web environment

Large-scale deployment of biometric systems for web-based services has to tackle technological issues related to security, interoperability and accuracy, and social issues related to privacy protection and biometric acquisition process acceptance. The variety of biometric traits, capturing devices, targeted populations and working scenarios makes the development of a universal solution for all-purpose deployment quite a difficult task.This paper describes the design, implementation and applicability of an open framework for distributed biometric authentication oriented to access-control in web environment. The open principle makes this framework a novel and practical development tool for testing and integrating biometric algorithms and devices from third parties. Special attention has been paid to security and interoperability standards to ease concurrent integration and testing of biometric trait matchers developed by different laboratories or companies. Finally, in order to demonstrate the versatility and usability of the framework, we describe the construction process for a distributed multibiometric database acquisition tool based on this framework.RésuméLa mise en marche à grande échelle de systèmes biométriques pour services web doit aborder des problèmes technologiques liés à la sécurité, l’interopérabilité et la précision, et les problèmes sociaux liés à la protection de l’intimité et l’acceptation du procédé d’acquisition. La variété des caractéristiques biométriques, des dispositifs d’acquisition, des populations ciblées et des scénarios de travail complique l’obtention d’une solution d’universelle de déploiement polyvalent.Cet article décrit la conception, la mise en œuvre et l’applicabilité d’un cadre ouvert pour une authentication biométrique distribuée destinée au contrôle d’accès dans un environnement web. Le principe ouvert fait de cette structure un outil de développement pratique et nouveau pour l’essai et l’intégration des algorithmes et dispositifs biométriques. On prête attention surtout aux normes de sécurité et d’interopérabilité qui facilitent l’intégration et l’essai de dispositifs biométriques développés par différents laboratoires ou compagnies. Le cadre intègre également nos outils de vérification de visage et de locuteur. Finalement, on décrit un outil web distribué d’acquisition de données biométriques basé sur notre cadre ouvert.

Multi-site heterogeneous system fusions for the Albayzin 2010 Language Recognition Evaluation

Best language recognition performance is commonly obtained by fusing the scores of several heterogeneous systems. Regardless the fusion approach, it is assumed that different systems may contribute complementary information, either because they are developed on different datasets, or because they use different features or different modeling approaches. Most authors apply fusion as a final resource for improving performance based on an existing set of systems. Though relative performance gains decrease as larger sets of systems are considered, best performance is usually attained by fusing all the available systems, which may lead to high computational costs. In this paper, we aim to discover which technologies combine the best through fusion and to analyse the factors (data, features, modeling methodologies, etc.) that may explain such a good performance. Results are presented and discussed for a number of systems provided by the participating sites and the organizing team of the Albayzin 2010 Language Recognition Evaluation. We hope the conclusions of this work help research groups make better decisions in developing language recognition technology.

Phonetic unit selection for cross-lingual query-by-example spoken term detection

Cross-lingual query-by-example spoken term detection (QbE STD) has caught the attention of speech researchers, as it makes it possible to develop systems for low-resource languages, in which the available amount of labelled data makes the training of automatic speech recognition approaches prohibitive. The use of phonetic posteriorgrams for speech representation combined with dynamic time warping search is a widely used approach for this task, but little attention has been focused in the suitability of a set of phonetic units to represent speech information spoken in a different language. This paper proposes a technique for estimating the relevance of phonetic units aiming at selecting the most suitable ones for a given target language. Experiments in a Spanish database using phoneme posteriorgrams in Czech, English, Hungarian and Russian proved the validity of the proposed method, as QbE STD performance was enhanced by reducing the set of phonetic units.

Novel strategies for reducing the false alarm rate in a speaker segmentation system

Reliable speaker segmentation is critical in many applications in the speech processing domain. In this paper, we extend our earlier formulation for false alarm reduction in a typical state-of-art speaker segmentation system. Specifically, we present two novel strategies for reducing the false alarm rate with a minimal impact on the true speaker change detection rate. One of the new strategies rejects, given a discard probability, those changes that are suspicious of being false alarms because of their low ΔBIC value; and the other one assumes that the occurrence of changes constitute a Poisson process, so changes will be discarded with a probability that follows a Poisson cumulative density function. Our experiments show the improvements obtained with each false alarm reduction approach using the Spanish Parliament Sessions defined for the 2006 TC-STAR Automatic Speech Recognition evaluation campaign.