Deep learning backend for single and multisession i-vector speaker recognition

The lack of labeled background data makes a big performance gap between cosine and Probabilistic Linear Discriminant Analysis (PLDA) scoring baseline techniques for i-vectors in speaker recognition. Although there are some unsupervised clustering techniques to estimate the labels, they cannot accurately predict the true labels and they also assume that there are several samples from the same speaker in the background data that could not be true in reality. In this paper, the authors make use of Deep Learning (DL) to fill this performance gap given unlabeled background data. To this goal, the authors have proposed an impostor selection algorithm and a universal model adaptation process in a hybrid system based on deep belief networks and deep neural networks to discriminatively model each target speaker. In order to have more insight into the behavior of DL techniques in both single- and multisession speaker enrollment tasks, some experiments have been carried out in this paper in both scenarios. Experiments on National Institute of Standards and Technology 2014 i-vector challenge show that 46% of this performance gap, in terms of minimum of the decision cost function, is filled by the proposed DL-based system. Furthermore, the score combination of the proposed DL-based system and PLDA with estimated labels covers 79% of this gap.Peer ReviewedPostprint (published version

Computer Graphics and Video Features for Speaker Recognition

Tato práce popisuje netradiční metodu rozpoznávání řečníka pomocí příznaků a alogoritmů používaných převážně v počítačovém vidění. V úvodu jsou shrnuty potřebné teoretické znalosti z oblasti počítačového rozpoznávání. Jako aplikace grafických příznaků v rozpoznávání řečníka jsou detailněji popsány již známé BBF příznaky. Tyto jsou vyhodnoceny nad standardními řečovými databázemi TIMIT a NIST SRE 2010. Experimentální výsledky jsou shrnuty a porovnány se standardními metodami. V závěru jsou jsou navrženy možné směry budoucí práce.We describe a non-traditional method for speaker recognition that uses features and algorithms used mainly for computer vision. Important theoretical knowledge of computer recognition is summarized first. The Boosted Binary Features are described and explored as an already proposed method, that has roots in computer vision. This method is evaluated on standard speaker recognition databases TIMIT and NIST SRE 2010. Experimental results are given and compared to standard methods. Possible directions for future work are proposed at the end.

Deep learning for i-vector speaker and language recognition

Over the last few years, i-vectors have been the state-of-the-art technique in speaker and language recognition. Recent advances in Deep Learning (DL) technology have improved the quality of i-vectors but the DL techniques in use are computationally expensive and need speaker or/and phonetic labels for the background data, which are not easily accessible in practice. On the other hand, the lack of speaker-labeled background data makes a big performance gap, in speaker recognition, between two well-known cosine and Probabilistic Linear Discriminant Analysis (PLDA) i-vector scoring techniques. It has recently been a challenge how to fill this gap without speaker labels, which are expensive in practice. Although some unsupervised clustering techniques are proposed to estimate the speaker labels, they cannot accurately estimate the labels. This thesis tries to solve the problems above by using the DL technology in different ways, without any need of speaker or phonetic labels. In order to fill the performance gap between cosine and PLDA scoring given unlabeled background data, we have proposed an impostor selection algorithm and a universal model adaptation process in a hybrid system based on Deep Belief Networks (DBNs) and Deep Neural Networks (DNNs) to discriminatively model each target speaker. In order to have more insight into the behavior of DL techniques in both single and multi-session speaker enrollment tasks, some experiments have been carried out in both scenarios. Experiments on the National Institute of Standard and Technology (NIST) 2014 i-vector challenge show that 46% of this performance gap, in terms of minDCF, is filled by the proposed DL-based system. Furthermore, the score combination of the proposed DL-based system and PLDA with estimated labels covers 79% of this gap. In the second line of the research, we have developed an efficient alternative vector representation of speech by keeping the computational cost as low as possible and avoiding phonetic labels, which are not always accessible. The proposed vectors will be based on both Gaussian Mixture Models (GMMs) and Restricted Boltzmann Machines (RBMs) and will be referred to as GMM-RBM vectors. The role of RBM is to learn the total speaker and session variability among background GMM supervectors. This RBM, which will be referred to as Universal RBM (URBM), will then be used to transform unseen supervectors to the proposed low dimensional vectors. The use of different activation functions for training the URBM and different transformation functions for extracting the proposed vectors are investigated. At the end, a variant of Rectified Linear Unit (ReLU) which is referred to as Variable ReLU (VReLU) is proposed. Experiments on the core test condition 5 of the NIST Speaker Recognition Evaluation (SRE) 2010 show that comparable results with conventional i-vectors are achieved with a clearly lower computational load in the vector extraction process. Finally, for the Language Identification (LID) application, we have proposed a DNN architecture to model effectively the i-vector space of four languages, English, Spanish, German, and Finnish, in the car environment. Both raw i-vectors and session variability compensated i-vectors are evaluated as input vectors to DNN. The performance of the proposed DNN architecture is compared with both conventional GMM-UBM and i-vector/Linear Discriminant Analysis (LDA) systems considering the effect of duration of signals. It is shown that the signals with duration between 2 and 3 sec meet the accuracy and speed requirements of this application, in which the proposed DNN architecture outperforms GMM-UBM and i-vector/LDA systems by 37% and 28%, respectively.En los últimos años, los i-vectores han sido la técnica de referencia en el reconocimiento de hablantes y de idioma. Los últimos avances en la tecnología de Aprendizaje Profundo (Deep Learning. DL) han mejorado la calidad de los i-vectores, pero las técnicas DL en uso son computacionalmente costosas y necesitan datos etiquetados para cada hablante y/o unidad fon ética, los cuales no son fácilmente accesibles en la práctica. La falta de datos etiquetados provoca una gran diferencia de los resultados en el reconocimiento de hablante con i-vectors entre las dos técnicas de evaluación más utilizados: distancia coseno y Análisis Lineal Discriminante Probabilístico (PLDA). Por el momento, sigue siendo un reto cómo reducir esta brecha sin disponer de las etiquetas de los hablantes, que son costosas de obtener. Aunque se han propuesto algunas técnicas de agrupamiento sin supervisión para estimar las etiquetas de los hablantes, no pueden estimar las etiquetas con precisión. Esta tesis trata de resolver los problemas mencionados usando la tecnología DL de diferentes maneras, sin necesidad de etiquetas de hablante o fon éticas. Con el fin de reducir la diferencia de resultados entre distancia coseno y PLDA a partir de datos no etiquetados, hemos propuesto un algoritmo selección de impostores y la adaptación a un modelo universal en un sistema hibrido basado en Deep Belief Networks (DBN) y Deep Neural Networks (DNN) para modelar a cada hablante objetivo de forma discriminativa. Con el fin de tener más información sobre el comportamiento de las técnicas DL en las tareas de identificación de hablante en una única sesión y en varias sesiones, se han llevado a cabo algunos experimentos en ambos escenarios. Los experimentos utilizando los datos del National Institute of Standard and Technology (NIST) 2014 i-vector Challenge muestran que el 46% de esta diferencia de resultados, en términos de minDCF, se reduce con el sistema propuesto basado en DL. Además, la combinación de evaluaciones del sistema propuesto basado en DL y PLDA con etiquetas estimadas reduce el 79% de esta diferencia. En la segunda línea de la investigación, hemos desarrollado una representación vectorial alternativa eficiente de la voz manteniendo el coste computacional lo más bajo posible y evitando las etiquetas fon éticas, Los vectores propuestos se basan tanto en el Modelo de Mezcla de Gaussianas (GMM) y en las Maquinas Boltzmann Restringidas (RBM), a los que se hacer referencia como vectores GMM-RBM. El papel de la RBM es aprender la variabilidad total del hablante y de la sesión entre los supervectores del GMM gen érico. Este RBM, al que se hará referencia como RBM Universal (URBM), se utilizará para transformar supervectores ocultos en los vectores propuestos, de menor dimensión. Además, se estudia el uso de diferentes funciones de activación para el entrenamiento de la URBM y diferentes funciones de transformación para extraer los vectores propuestos. Finalmente, se propone una variante de la Unidad Lineal Rectificada (ReLU) a la que se hace referencia como Variable ReLU (VReLU). Los experimentos sobre los datos de la condición 5 del test de la NIST Speaker Recognition Evaluation (SRE) 2010 muestran que se han conseguidos resultados comparables con los i-vectores convencionales, con una carga computacional claramente inferior en el proceso de extracción de vectores. Por último, para la aplicación de Identificación de Idioma (LID), hemos propuesto una arquitectura DNN para modelar eficazmente en el entorno del coche el espacio i-vector de cuatro idiomas: inglés, español, alemán y finlandés. Tanto los i-vectores originales como los i-vectores propuestos son evaluados como vectores de entrada a DNN. El rendimiento de la arquitectura DNN propuesta se compara con los sistemas convencionales GMM-UBM y i-vector/Análisis Discriminante Lineal (LDA) considerando el efecto de la duración de las señales. Se muestra que en caso de señales con una duración entre 2 y 3 se obtienen resultados satisfactorios en cuanto a precisión y resultados, superando a los sistemas GMM-UBM y i-vector/LDA en un 37% y 28%, respectivament

The L2F - UPC Speaker Recognition System for NIST SRE 2010

This document describes the joint submission of the INESC-ID’s Spoken Language Systems Laboratory (L 2 F) and the TALP Research Center from the Technical University of Catalonia (UPC) to the 2010 NIST Speaker Recognition evaluation. The L2F-UPC primary system is composed by the fusion of five individual sub-systems. Speaker recognition results have been submitted only for the core-core conditionPostprint (published version

Bayesian distance metric learning and its application in automatic speaker recognition systems

This paper proposes state-of the-art Automatic Speaker Recognition System (ASR) based on Bayesian Distance Learning Metric as a feature extractor. In this modeling, I explored the constraints of the distance between modified and simplified i-vector pairs by the same speaker and different speakers. An approximation of the distance metric is used as a weighted covariance matrix from the higher eigenvectors of the covariance matrix, which is used to estimate the posterior distribution of the metric distance. Given a speaker tag, I select the data pair of the different speakers with the highest cosine score to form a set of speaker constraints. This collection captures the most discriminating variability between the speakers in the training data. This Bayesian distance learning approach achieves better performance than the most advanced methods. Furthermore, this method is insensitive to normalization compared to cosine scores. This method is very effective in the case of limited training data. The modified supervised i-vector based ASR system is evaluated on the NIST SRE 2008 database. The best performance of the combined cosine score EER 1.767% obtained using LDA200 + NCA200 + LDA200, and the best performance of Bayes_dml EER 1.775% obtained using LDA200 + NCA200 + LDA100. Bayesian_dml overcomes the combined norm of cosine scores and is the best result of the short2-short3 condition report for NIST SRE 2008 data

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

Subspace and graph methods to leverage auxiliary data for limited target data multi-class classification, applied to speaker verification

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 127-130).Multi-class classification can be adversely affected by the absence of sufficient target (in-class) instances for training. Such cases arise in face recognition, speaker verification, and document classification, among others. Auxiliary data-sets, which contain a diverse sampling of non-target instances, are leveraged in this thesis using subspace and graph methods to improve classification where target data is limited. The auxiliary data is used to define a compact representation that maps instances into a vector space where inner products quantify class similarity. Within this space, an estimate of the subspace that constitutes within-class variability (e.g. the recording channel in speaker verification or the illumination conditions in face recognition) can be obtained using class-labeled auxiliary data. This thesis proposes a way to incorporate this estimate into the SVM framework to perform nuisance compensation, thus improving classification performance. Another contribution is a framework that combines mapping and compensation into a single linear comparison, which motivates computationally inexpensive and accurate comparison functions. A key aspect of the work takes advantage of efficient pairwise comparisons between the training, test, and auxiliary instances to characterize their interaction within the vector space, and exploits it for improved classification in three ways. The first uses the local variability around the train and test instances to reduce false-alarms. The second assumes the instances lie on a low-dimensional manifold and uses the distances along the manifold. The third extracts relational features from a similarity graph where nodes correspond to the training, test and auxiliary instances. To quantify the merit of the proposed techniques, results of experiments in speaker verification are presented where only a single target recording is provided to train the classifier. Experiments are preformed on standard NIST corpora and methods are compared using standard evalutation metrics: detection error trade-off curves, minimum decision costs, and equal error rates.by Zahi Nadim Karam.Ph.D

Discriminative and generative approaches for long- and short-term speaker characteristics modeling : application to speaker verification

The speaker verification problem can be stated as follows: given two speech recordings, determine whether or not they have been uttered by the same speaker. Most current speaker verification systems are based on Gaussian mixture models. This probabilistic representation allows to adequately model the complex distribution of the underlying speech feature parameters. It however represents an inadequate basis for discriminating between speakers, which is the key issue in the area of speaker verification. In the first part of this thesis, we attempt to overcome these difficulties by proposing to combine support vector machines, a well established discriminative modeling, with two generative approaches based on Gaussian mixture models. In the first generative approach, a target speaker is represented by a Gaussian mixture model corresponding to a Maximum A Posteriori adaptation of a large Gaussian mixture model, coined universal background model, to the target speaker data. The second generative approach is the Joint Factor Analysis that has become the state-of-the-art in the field of speaker verification during the last three years. The advantage of this technique is that it provides a framework of powerful tools for modeling the inter-speaker and channel variabilities. We propose and test several kernel functions that are integrated in the design of both previous combinations. The best results are obtained when the support vector machines are applied within a new space called the "total variability space", defined using the factor analysis. In this novel modeling approach, the channel effect is treated through a combination of linear discnminant analysis and kemel normalization based on the inverse of the within covariance matrix of the speaker. In the second part of this thesis, we present a new approach to modeling the speaker's longterm prosodic and spectral characteristics. This novel approach is based on continuous approximations of the prosodic and cepstral contours contained in a pseudo-syllabic segment of speech. Each of these contours is fitted to a Legendre polynomial, whose coefficients are modeled by a Gaussian mixture model. The joint factor analysis is used to treat the speaker and channel variabilities. Finally, we perform a scores fusion between systems based on long-term speaker characteristics with those described above that use short-term speaker features