Analysis of Speaker Verification System Using Support Vector Machine

The integration of GMM- super vector and Support Vector Machine (SVM) has become one of most popular strategy in text-independent speaker verification system.  This paper describes the application of Fuzzy Support Vector Machine (FSVM) for classification of speakers using GMM-super vectors. Super vectors are formed by stacking the mean vectors of adapted GMMs from UBM using maximum a posteriori (MAP). GMM super vectors characterize speaker’s acoustic characteristics which are used for developing a speaker dependent fuzzy SVM model. Introducing fuzzy theory in support vector machine yields better classification accuracy and requires less number of support vectors. Experiments were conducted on 2001 NIST speaker recognition evaluation corpus. Performance of GMM-FSVM based speaker verification system is compared with the conventional GMM-UBM and GMM-SVM based systems.  Experimental results indicate that the fuzzy SVM based speaker verification system with GMM super vector achieves better performance to GMM-UBM system. Â

Scalable learning for geostatistics and speaker recognition

With improved data acquisition methods, the amount of data that is being collected has increased severalfold. One of the objectives in data collection is to learn useful underlying patterns. In order to work with data at this scale, the methods not only need to be effective with the underlying data, but also have to be scalable to handle larger data collections. This thesis focuses on developing scalable and effective methods targeted towards different domains, geostatistics and speaker recognition in particular. Initially we focus on kernel based learning methods and develop a GPU based parallel framework for this class of problems. An improved numerical algorithm that utilizes the GPU parallelization to further enhance the computational performance of kernel regression is proposed. These methods are then demonstrated on problems arising in geostatistics and speaker recognition. In geostatistics, data is often collected at scattered locations and factors like instrument malfunctioning lead to missing observations. Applications often require the ability interpolate this scattered spatiotemporal data on to a regular grid continuously over time. This problem can be formulated as a regression problem, and one of the most popular geostatistical interpolation techniques, kriging is analogous to a standard kernel method: Gaussian process regression. Kriging is computationally expensive and needs major modifications and accelerations in order to be used practically. The GPU framework developed for kernel methods is extended to kriging and further the GPU's texture memory is better utilized for enhanced computational performance. Speaker recognition deals with the task of verifying a person's identity based on samples of his/her speech - "utterances". This thesis focuses on text-independent framework and three new recognition frameworks were developed for this problem. We proposed a kernelized Renyi distance based similarity scoring for speaker recognition. While its performance is promising, it does not generalize well for limited training data and therefore does not compare well to state-of-the-art recognition systems. These systems compensate for the variability in the speech data due to the message, channel variability, noise and reverberation. State-of-the-art systems model each speaker as a mixture of Gaussians (GMM) and compensate for the variability (termed "nuisance"). We propose a novel discriminative framework using a latent variable technique, partial least squares (PLS), for improved recognition. The kernelized version of this algorithm is used to achieve a state of the art speaker ID system, that shows results competitive with the best systems reported on in NIST's 2010 Speaker Recognition Evaluation

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

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

Pattern mining approaches used in sensor-based biometric recognition: a review

Sensing technologies place significant interest in the use of biometrics for the recognition and assessment of individuals. Pattern mining techniques have established a critical step in the progress of sensor-based biometric systems that are capable of perceiving, recognizing and computing sensor data, being a technology that searches for the high-level information about pattern recognition from low-level sensor readings in order to construct an artificial substitute for human recognition. The design of a successful sensor-based biometric recognition system needs to pay attention to the different issues involved in processing variable data being - acquisition of biometric data from a sensor, data pre-processing, feature extraction, recognition and/or classification, clustering and validation. A significant number of approaches from image processing, pattern identification and machine learning have been used to process sensor data. This paper aims to deliver a state-of-the-art summary and present strategies for utilizing the broadly utilized pattern mining methods in order to identify the challenges as well as future research directions of sensor-based biometric systems

Acoustic Approaches to Gender and Accent Identification

There has been considerable research on the problems of speaker and language recognition from samples of speech. A less researched problem is that of accent recognition. Although this is a similar problem to language identification, di�erent accents of a language exhibit more fine-grained di�erences between classes than languages. This presents a tougher problem for traditional classification techniques. In this thesis, we propose and evaluate a number of techniques for gender and accent classification. These techniques are novel modifications and extensions to state of the art algorithms, and they result in enhanced performance on gender and accent recognition. The first part of the thesis focuses on the problem of gender identification, and presents a technique that gives improved performance in situations where training and test conditions are mismatched. The bulk of this thesis is concerned with the application of the i-Vector technique to accent identification, which is the most successful approach to acoustic classification to have emerged in recent years. We show that it is possible to achieve high accuracy accent identification without reliance on transcriptions and without utilising phoneme recognition algorithms. The thesis describes various stages in the development of i-Vector based accent classification that improve the standard approaches usually applied for speaker or language identification, which are insu�cient. We demonstrate that very good accent identification performance is possible with acoustic methods by considering di�erent i-Vector projections, frontend parameters, i-Vector configuration parameters, and an optimised fusion of the resulting i-Vector classifiers we can obtain from the same data. We claim to have achieved the best accent identification performance on the test corpus for acoustic methods, with up to 90% identification rate. This performance is even better than previously reported acoustic-phonotactic based systems on the same corpus, and is very close to performance obtained via transcription based accent identification. Finally, we demonstrate that the utilization of our techniques for speech recognition purposes leads to considerably lower word error rates. Keywords: Accent Identification, Gender Identification, Speaker Identification, Gaussian Mixture Model, Support Vector Machine, i-Vector, Factor Analysis, Feature Extraction, British English, Prosody, Speech Recognition

Advances in Subspace-based Solutions for Diarization in the Broadcast Domain

La motivación de esta tesis es la necesidad de soluciones robustas al problema de diarización. Estas técnicas de diarización deben proporcionar valor añadido a la creciente cantidad disponible de datos multimedia mediante la precisa discriminación de los locutores presentes en la señal de audio. Desafortunadamente, hasta tiempos recientes este tipo de tecnologías solamente era viable en condiciones restringidas, quedando por tanto lejos de una solución general. Las razones detrás de las limitadas prestaciones de los sistemas de diarización son múltiples. La primera causa a tener en cuenta es la alta complejidad de la producción de la voz humana, en particular acerca de los procesos fisiológicos necesarios para incluir las características discriminativas de locutor en la señal de voz. Esta complejidad hace del proceso inverso, la estimación de dichas características a partir del audio, una tarea ineficiente por medio de las técnicas actuales del estado del arte. Consecuentemente, en su lugar deberán tenerse en cuenta aproximaciones. Los esfuerzos en la tarea de modelado han proporcionado modelos cada vez más elaborados, aunque no buscando la explicación última de naturaleza fisiológica de la señal de voz. En su lugar estos modelos aprenden relaciones entre la señales acústicas a partir de un gran conjunto de datos de entrenamiento. El desarrollo de modelos aproximados genera a su vez una segunda razón, la variabilidad de dominio. Debido al uso de relaciones aprendidas a partir de un conjunto de entrenamiento concreto, cualquier cambio de dominio que modifique las condiciones acústicas con respecto a los datos de entrenamiento condiciona las relaciones asumidas, pudiendo causar fallos consistentes en los sistemas.Nuestra contribución a las tecnologías de diarización se ha centrado en el entorno de radiodifusión. Este dominio es actualmente un entorno todavía complejo para los sistemas de diarización donde ninguna simplificación de la tarea puede ser tenida en cuenta. Por tanto, se deberá desarrollar un modelado eficiente del audio para extraer la información de locutor y como inferir el etiquetado correspondiente. Además, la presencia de múltiples condiciones acústicas debido a la existencia de diferentes programas y/o géneros en el domino requiere el desarrollo de técnicas capaces de adaptar el conocimiento adquirido en un determinado escenario donde la información está disponible a aquellos entornos donde dicha información es limitada o sencillamente no disponible.Para este propósito el trabajo desarrollado a lo largo de la tesis se ha centrado en tres subtareas: caracterización de locutor, agrupamiento y adaptación de modelos. La primera subtarea busca el modelado de un fragmento de audio para obtener representaciones precisas de los locutores involucrados, poniendo de manifiesto sus propiedades discriminativas. En este área se ha llevado a cabo un estudio acerca de las actuales estrategias de modelado, especialmente atendiendo a las limitaciones de las representaciones extraídas y poniendo de manifiesto el tipo de errores que pueden generar. Además, se han propuesto alternativas basadas en redes neuronales haciendo uso del conocimiento adquirido. La segunda tarea es el agrupamiento, encargado de desarrollar estrategias que busquen el etiquetado óptimo de los locutores. La investigación desarrollada durante esta tesis ha propuesto nuevas estrategias para estimar el mejor reparto de locutores basadas en técnicas de subespacios, especialmente PLDA. Finalmente, la tarea de adaptación de modelos busca transferir el conocimiento obtenido de un conjunto de entrenamiento a dominios alternativos donde no hay datos para extraerlo. Para este propósito los esfuerzos se han centrado en la extracción no supervisada de información de locutor del propio audio a diarizar, sinedo posteriormente usada en la adaptación de los modelos involucrados.<br /

Feature Selection and Classifier Development for Radio Frequency Device Identification

The proliferation of simple and low-cost devices, such as IEEE 802.15.4 ZigBee and Z-Wave, in Critical Infrastructure (CI) increases security concerns. Radio Frequency Distinct Native Attribute (RF-DNA) Fingerprinting facilitates biometric-like identification of electronic devices emissions from variances in device hardware. Developing reliable classifier models using RF-DNA fingerprints is thus important for device discrimination to enable reliable Device Classification (a one-to-many looks most like assessment) and Device ID Verification (a one-to-one looks how much like assessment). AFITs prior RF-DNA work focused on Multiple Discriminant Analysis/Maximum Likelihood (MDA/ML) and Generalized Relevance Learning Vector Quantized Improved (GRLVQI) classifiers. This work 1) introduces a new GRLVQI-Distance (GRLVQI-D) classifier that extends prior GRLVQI work by supporting alternative distance measures, 2) formalizes a framework for selecting competing distance measures for GRLVQI-D, 3) introducing response surface methods for optimizing GRLVQI and GRLVQI-D algorithm settings, 4) develops an MDA-based Loadings Fusion (MLF) Dimensional Reduction Analysis (DRA) method for improved classifier-based feature selection, 5) introduces the F-test as a DRA method for RF-DNA fingerprints, 6) provides a phenomenological understanding of test statistics and p-values, with KS-test and F-test statistic values being superior to p-values for DRA, and 7) introduces quantitative dimensionality assessment methods for DRA subset selection

Robust text independent closed set speaker identification systems and their evaluation

PhD ThesisThis thesis focuses upon text independent closed set speaker identi cation. The contributions relate to evaluation studies in the presence of various types of noise and handset e ects. Extensive evaluations are performed on four databases. The rst contribution is in the context of the use of the Gaussian Mixture Model-Universal Background Model (GMM-UBM) with original speech recordings from only the TIMIT database. Four main simulations for Speaker Identi cation Accuracy (SIA) are presented including di erent fusion strategies: Late fusion (score based), early fusion (feature based) and early-late fusion (combination of feature and score based), late fusion using concatenated static and dynamic features (features with temporal derivatives such as rst order derivative delta and second order derivative delta-delta features, namely acceleration features), and nally fusion of statistically independent normalized scores. The second contribution is again based on the GMM-UBM approach. Comprehensive evaluations of the e ect of Additive White Gaussian Noise (AWGN), and Non-Stationary Noise (NSN) (with and without a G.712 type handset) upon identi cation performance are undertaken. In particular, three NSN types with varying Signal to Noise Ratios (SNRs) were tested corresponding to: street tra c, a bus interior and a crowded talking environment. The performance evaluation also considered the e ect of late fusion techniques based on score fusion, namely mean, maximum, and linear weighted sum fusion. The databases employed were: TIMIT, SITW, and NIST 2008; and 120 speakers were selected from each database to yield 3,600 speech utterances. The third contribution is based on the use of the I-vector, four combinations of I-vectors with 100 and 200 dimensions were employed. Then, various fusion techniques using maximum, mean, weighted sum and cumulative fusion with the same I-vector dimension were used to improve the SIA. Similarly, both interleaving and concatenated I-vector fusion were exploited to produce 200 and 400 I-vector dimensions. The system was evaluated with four di erent databases using 120 speakers from each database. TIMIT, SITW and NIST 2008 databases were evaluated for various types of NSN namely, street-tra c NSN, bus-interior NSN and crowd talking NSN; and the G.712 type handset at 16 kHz was also applied. As recommendations from the study in terms of the GMM-UBM approach, mean fusion is found to yield overall best performance in terms of the SIA with noisy speech, whereas linear weighted sum fusion is overall best for original database recordings. However, in the I-vector approach the best SIA was obtained from the weighted sum and the concatenated fusion.Ministry of Higher Education and Scienti c Research (MoHESR), and the Iraqi Cultural Attach e, Al-Mustansiriya University, Al-Mustansiriya University College of Engineering in Iraq for supporting my PhD scholarship