VOICE BIOMETRICS UNDER MISMATCHED NOISE CONDITIONS

This thesis describes research into effective voice biometrics (speaker recognition) under mismatched noise conditions. Over the last two decades, this class of biometrics has been the subject of considerable research due to its various applications in such areas as telephone banking, remote access control and surveillance. One of the main challenges associated with the deployment of voice biometrics in practice is that of undesired variations in speech characteristics caused by environmental noise. Such variations can in turn lead to a mismatch between the corresponding test and reference material from the same speaker. This is found to adversely affect the performance of speaker recognition in terms of accuracy. To address the above problem, a novel approach is introduced and investigated. The proposed method is based on minimising the noise mismatch between reference speaker models and the given test utterance, and involves a new form of Test-Normalisation (T-Norm) for further enhancing matching scores under the aforementioned adverse operating conditions. Through experimental investigations, based on the two main classes of speaker recognition (i.e. verification/ open-set identification), it is shown that the proposed approach can significantly improve the performance accuracy under mismatched noise conditions. In order to further improve the recognition accuracy in severe mismatch conditions, an approach to enhancing the above stated method is proposed. This, which involves providing a closer adjustment of the reference speaker models to the noise condition in the test utterance, is shown to considerably increase the accuracy in extreme cases of noisy test data. Moreover, to tackle the computational burden associated with the use of the enhanced approach with open-set identification, an efficient algorithm for its realisation in this context is introduced and evaluated. The thesis presents a detailed description of the research undertaken, describes the experimental investigations and provides a thorough analysis of the outcomes

Memory and computation effective approaches for i-vector extraction

This paper focuses on the extraction of i-vectors, a compact representation of spoken utterances that is used by most of the state-of-the-art speaker recognition systems. This work was mainly motivated by the need of reducing the memory demand of the huge data structures that are usually precomputed for fast computation of the i-vectors. We propose a set of new approaches allowing accurate i-vector extraction but requiring less memory, showing their relations with the standard computation method introduced for eigenvoices. We analyze the time and memory resources required by these solutions, which are suited to different fields of application, and we show that it is possible to get accurate results with solutions that reduce both computation time and memory demand compared with the standard solutio

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

Memory and computation trade-offs for efficient i-vector extraction

This work aims at reducing the memory demand of the data structures that are usually pre-computed and stored for fast computation of the i-vectors, a compact representation of spoken utterances that is used by most state-of-the-art speaker recognition systems. We propose two new approaches allowing accurate i-vector extraction but requiring less memory, showing their relations with the standard computation method introduced for eigenvoices, and with the recently proposed fast eigen-decomposition technique. The first approach computes an i-vector in a Variational Bayes (VB) framework by iterating the estimation of one sub-block of i-vector elements at a time, keeping fixed all the others, and can obtain i-vectors as accurate as the ones obtained by the standard technique but requiring only 25% of its memory. The second technique is based on the Conjugate Gradient solution of a linear system, which is accurate and uses even less memory, but is slower than the VB approach. We analyze and compare the time and memory resources required by all these solutions, which are suited to different applications, and we show that it is possible to get accurate results greatly reducing memory demand compared with the standard solution at almost the same speed

Memory and computation effective approaches for i–vector extraction

This paper focuses on the extraction of i-vectors, a compact representation of spoken utterances that is used by most of the state–of–the–art speaker recognition systems. This work was mainly motivated by the need of reducing the memory demand of the huge data structures that are usually precomputed for fast computation of the i-vectors. We propose a set of new approaches allowing accurate i-vector extraction but requiring less memory, showing their relations with the standard computation method introduced for eigenvoices. We analyze the time and memory resources required by these solutions, which are suited to different fields of application, and we show that it is possible to get accurate results with solutions that reduce both computation time and memory demand compared with the standard solution

Advanced Biometrics with Deep Learning

Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others