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

Evaluating automatic speaker recognition systems: an overview of the nist speaker recognition evaluations (1996-2014)

2014 CSIC. Manuscripts published in this Journal are the property of the Consejo Superior de Investigaciones Científicas, and quoting this source is a requirement for any partial or full reproduction.Automatic Speaker Recognition systems show interesting properties, such as speed of processing or repeatability of results, in contrast to speaker recognition by humans. But they will be usable just if they are reliable. Testability, or the ability to extensively evaluate the goodness of the speaker detector decisions, becomes then critical. In the last 20 years, the US National Institute of Standards and Technology (NIST) has organized, providing the proper speech data and evaluation protocols, a series of text-independent Speaker Recognition Evaluations (SRE). Those evaluations have become not just a periodical benchmark test, but also a meeting point of a collaborative community of scientists that have been deeply involved in the cycle of evaluations, allowing tremendous progress in a specially complex task where the speaker information is spread across different information levels (acoustic, prosodic, linguistic…) and is strongly affected by speaker intrinsic and extrinsic variability factors. In this paper, we outline how the evaluations progressively challenged the technology including new speaking conditions and sources of variability, and how the scientific community gave answers to those demands. Finally, NIST SREs will be shown to be not free of inconveniences, and future challenges to speaker recognition assessment will also be discussed

Audio segmentation-by-classification approach based on factor analysis in broadcast news domain

This paper studies a novel audio segmentation-by-classification approach based on factor analysis. The proposed technique compensates the within-class variability by using class-dependent factor loading matrices and obtains the scores by computing the log-likelihood ratio for the class model to a non-class model over fixed-length windows. Afterwards, these scores are smoothed to yield longer contiguous segments of the same class by means of different back-end systems. Unlike previous solutions, our proposal does not make use of specific acoustic features and does not need a hierarchical structure. The proposed method is applied to segment and classify audios coming from TV shows into five different acoustic classes: speech, music, speech with music, speech with noise, and others. The technique is compared to a hierarchical system with specific acoustic features achieving a significant error reduction

Métodos discriminativos para la optimización de modelos en la Verificación del Hablante

La creciente necesidad de sistemas de autenticación seguros ha motivado el interés de algoritmos efectivos de Verificación de Hablante (VH). Dicha necesidad de algoritmos de alto rendimiento, capaces de obtener tasas de error bajas, ha abierto varias ramas de investigación. En este trabajo proponemos investigar, desde un punto de vista discriminativo, un conjunto de metodologías para mejorar el desempeño del estado del arte de los sistemas de VH. En un primer enfoque investigamos la optimización de los hiper-parámetros para explícitamente considerar el compromiso entre los errores de falsa aceptación y falso rechazo. El objetivo de la optimización se puede lograr maximizando el área bajo la curva conocida como ROC (Receiver Operating Characteristic) por sus siglas en inglés. Creemos que esta optimización de los parámetros no debe de estar limitada solo a un punto de operación y una estrategia más robusta es optimizar los parámetros para incrementar el área bajo la curva, AUC (Area Under the Curve por sus siglas en inglés) de modo que todos los puntos sean maximizados. Estudiaremos cómo optimizar los parámetros utilizando la representación matemática del área bajo la curva ROC basada en la estadística de Wilcoxon Mann Whitney (WMW) y el cálculo adecuado empleando el algoritmo de descendente probabilístico generalizado. Además, analizamos el efecto y mejoras en métricas como la curva detection error tradeoff (DET), el error conocido como Equal Error Rate (EER) y el valor mínimo de la función de detección de costo, minimum value of the detection cost function (minDCF) todos ellos por sue siglas en inglés. En un segundo enfoque, investigamos la señal de voz como una combinación de atributos que contienen información del hablante, del canal y el ruido. Los sistemas de verificación convencionales entrenan modelos únicos genéricos para todos los casos, y manejan las variaciones de estos atributos ya sea usando análisis de factores o no considerando esas variaciones de manera explícita. Proponemos una nueva metodología para particionar el espacio de los datos de acuerdo a estas carcterísticas y entrenar modelos por separado para cada partición. Las particiones se pueden obtener de acuerdo a cada atributo. En esta investigación mostraremos como entrenar efectivamente los modelos de manera discriminativa para maximizar la separación entre ellos. Además, el diseño de algoritimos robustos a las condiciones de ruido juegan un papel clave que permite a los sistemas de VH operar en condiciones reales. Proponemos extender nuestras metodologías para mitigar los efectos del ruido en esas condiciones. Para nuestro primer enfoque, en una situación donde el ruido se encuentre presente, el punto de operación puede no ser solo un punto, o puede existir un corrimiento de forma impredecible. Mostraremos como nuestra metodología de maximización del área bajo la curva ROC es más robusta que la usada por clasificadores convencionales incluso cuando el ruido no está explícitamente considerado. Además, podemos encontrar ruido a diferentes relación señal a ruido (SNR) que puede degradar el desempeño del sistema. Así, es factible considerar una descomposición eficiente de las señales de voz que tome en cuenta los diferentes atributos como son SNR, el ruido y el tipo de canal. Consideramos que en lugar de abordar el problema con un modelo unificado, una descomposición en particiones del espacio de características basado en atributos especiales puede proporcionar mejores resultados. Esos atributos pueden representar diferentes canales y condiciones de ruido. Hemos analizado el potencial de estas metodologías que permiten mejorar el desempeño del estado del arte de los sistemas reduciendo el error, y por otra parte controlar los puntos de operación y mitigar los efectos del ruido

On the Distribution of Speaker Verification Scores: Generative Models for Unsupervised Calibration

Speaker verification systems whose outputs can be interpreted as log-likelihood ratios (LLR) allow for cost-effective decisions by comparing the system outputs to application-defined thresholds depending only on prior information. Classifiers often produce uncalibrated scores, and require additional processing to produce well-calibrated LLRs. Recently, generative score calibration models have been proposed, which achieve calibration performance close to that of state-of-the-art discriminative techniques for supervised scenarios, while also allowing for unsupervised training. The effectiveness of these methods, however, strongly depends on their capabilities to correctly model the target and non-target score distributions. In this work we propose theoretically grounded and accurate models for characterizing the distribution of scores of speaker verification systems. Our approach is based on tied Generalized Hyperbolic distributions and overcomes many limitations of Gaussian models. Experimental results on different NIST benchmarks, using different utterance representation front-ends and different back-end classifiers, show that our method is effective not only in supervised scenarios, but also in unsupervised tasks characterized by very low proportion of target trials

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

Frame-level features conveying phonetic information for language and speaker recognition

150 p.This Thesis, developed in the Software Technologies Working Group of the Departmentof Electricity and Electronics of the University of the Basque Country, focuseson the research eld of spoken language and speaker recognition technologies.More specically, the research carried out studies the design of a set of featuresconveying spectral acoustic and phonotactic information, searches for the optimalfeature extraction parameters, and analyses the integration and usage of the featuresin language recognition systems, and the complementarity of these approacheswith regard to state-of-the-art systems. The study reveals that systems trained onthe proposed set of features, denoted as Phone Log-Likelihood Ratios (PLLRs), arehighly competitive, outperforming in several benchmarks other state-of-the-art systems.Moreover, PLLR-based systems also provide complementary information withregard to other phonotactic and acoustic approaches, which makes them suitable infusions to improve the overall performance of spoken language recognition systems.The usage of this features is also studied in speaker recognition tasks. In this context,the results attained by the approaches based on PLLR features are not as remarkableas the ones of systems based on standard acoustic features, but they still providecomplementary information that can be used to enhance the overall performance ofthe speaker recognition systems

Discriminative classifiers for speaker recognition

Speaker Recognition, Speaker Verification, Sparse Kernel Logistic Regression, Support Vector MachineMagdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2008von Marcel Kat