Implementation and evaluation of a low complexity microphone array for speaker recognition

Includes bibliographical references (leaves 83-86).This thesis discusses the application of a microphone array employing a noise canceling beamforming technique for improving the robustness of speaker recognition systems in a diffuse noise field

A non-linear polynomial approximation filter for robust speaker verification

Bibliography: leaves 101-109

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

Robust speaker recognition in presence of non-trivial environmental noise (toward greater biometric security)

The aim of this thesis is to investigate speaker recognition in the presence of environmental noise, and to develop a robust speaker recognition method. Recently, Speaker Recognition has been the object of considerable research due to its wide use in various areas. Despite major developments in this field, there are still many limitations and challenges. Environmental noises and their variations are high up in the list of challenges since it impossible to provide a noise free environment. A novel approach is proposed to address the issue of performance degradation in environmental noise. This approach is based on the estimation of signal-to-noise ratio (SNR) and detection of ambient noise from the recognition signal to re-train the reference model for the claimed speaker and to generate a new adapted noisy model to decrease the noise mismatch with recognition utterances. This approach is termed “Training on the fly” for robustness of speaker recognition under noisy environments. To detect the noise in the recognition signal two different techniques are proposed: the first technique including generating an emulated noise depending on estimated power spectrum of the original noise using 1/3 octave band filter bank and white noise signal. This emulated noise become close enough to original one that includes in the input signal (recognition signal). The second technique deals with extracting the noise from the input signal using one of speech enhancement algorithm with spectral subtraction to find the noise in the signal. Training on the fly approach (using both techniques) has been examined using two feature approaches and two different kinds of artificial clean and noisy speech databases collected in different environments. Furthermore, the speech samples were text independent. The training on the fly approach is a significant improvement in performance when compared with the performance of conventional speaker recognition (based on clean reference models). Moreover, the training on the fly based on noise extraction showed the best results for all types of noisy data