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

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

Local representations and random sampling for speaker verification

In text-independent speaker verification, studies focused on compensating intra-speaker variabilities at the modeling stage through the last decade. Intra-speaker variabilities may be due to channel effects, phonetic content or the speaker himself in the form of speaking style, emotional state, health or other similar factors. Joint Factor Analysis, Total Variability Space compensation, Nuisance Attribute Projection are some of the most successful approaches for inter-session variability compensation in the literature. In this thesis, we criticize the assumptions of low dimensionality of channel space in these methods and propose to partition the acoustic space into local regions. Intra-speaker variability compensation may be done in each local space separately. Two architectures are proposed depending on whether the subsequent modeling and scoring steps will also be done locally or globally. We have also focused on a particular component of intra-speaker variability, namely within-session variability. The main source of within-session variability is the differences in the phonetic content of speech segments in a single utterance. The variabilities in phonetic content may be either due to across acoustic event variabilities or due to differences in the actual realizations of the acoustic events. We propose a method to combat these variabilities through random sampling of training utterance. The method is shown to be effective both in short and long test utterances

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

Reconnaissance automatique du locuteur par des GMM à grande marge

Depuis plusieurs dizaines d'années, la reconnaissance automatique du locuteur (RAL) fait l'objet de travaux de recherche entrepris par de nombreuses équipes dans le monde. La majorité des systèmes actuels sont basés sur l'utilisation des Modèles de Mélange de lois Gaussiennes (GMM) et/ou des modèles discriminants SVM, i.e., les machines à vecteurs de support. Nos travaux ont pour objectif général la proposition d'utiliser de nouveaux modèles GMM à grande marge pour la RAL qui soient une alternative aux modèles GMM génératifs classiques et à l'approche discriminante état de l'art GMM-SVM. Nous appelons ces modèles LM-dGMM pour Large Margin diagonal GMM. Nos modèles reposent sur une récente technique discriminante pour la séparation multi-classes, qui a été appliquée en reconnaissance de la parole. Exploitant les propriétés des systèmes GMM utilisés en RAL, nous présentons dans cette thèse des variantes d'algorithmes d'apprentissage discriminant des GMM minimisant une fonction de perte à grande marge. Des tests effectués sur les tâches de reconnaissance du locuteur de la campagne d'évaluation NIST-SRE 2006 démontrent l'intérêt de ces modèles en reconnaissance.Most of state-of-the-art speaker recognition systems are based on Gaussian Mixture Models (GMM), trained using maximum likelihood estimation and maximum a posteriori (MAP) estimation. The generative training of the GMM does not however directly optimize the classification performance. For this reason, discriminative models, e.g., Support Vector Machines (SVM), have been an interesting alternative since they address directly the classification problem, and they lead to good performances. Recently a new discriminative approach for multiway classification has been proposed, the Large Margin Gaussian mixture models (LM-GMM). As in SVM, the parameters of LM-GMM are trained by solving a convex optimization problem. However they differ from SVM by using ellipsoids to model the classes directly in the input space, instead of half-spaces in an extended high-dimensional space. While LM-GMM have been used in speech recognition, they have not been used in speaker recognition (to the best of our knowledge). In this thesis, we propose simplified, fast and more efficient versions of LM-GMM which exploit the properties and characteristics of speaker recognition applications and systems, the LM-dGMM models. In our LM-dGMM modeling, each class is initially modeled by a GMM trained by MAP adaptation of a Universal Background Model (UBM) or directly initialized by the UBM. The models mean vectors are then re-estimated under some Large Margin constraints. We carried out experiments on full speaker recognition tasks under the NIST-SRE 2006 core condition. The experimental results are very satisfactory and show that our Large Margin modeling approach is very promising