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

The use of spectral information in the development of novel techniques for speech-based cognitive load classification

The cognitive load of a user refers to the amount of mental demand imposed on the user when performing a particular task. Estimating the cognitive load (CL) level of the users is necessary to adjust the workload imposed on them accordingly in order to improve task performance. The current speech based CL classification systems are not adequate for commercial use due to their low performance particularly in noisy environments. This thesis proposes many techniques to improve the performance of the speech based cognitive load classification system in both clean and noisy conditions. This thesis analyses and presents the effectiveness of speech features such as spectral centroid frequency (SCF) and spectral centroid amplitude (SCA) for CL classification. Sub-systems based on SCF and SCA features were developed and fused with the traditional Mel frequency cepstral coefficients (MFCC) based system, producing an 8.9% and 31.5% relative error rate reduction respectively when compared to the MFCC-based system alone. The Stroop test corpus was used in these experiments. The investigation into cognitive load information in the form of spectral distribution in different subbands shows that the information distributed in the low frequency subband is significantly higher than the high frequency subband. Two different methods are proposed to utilize this finding. The first method, called the multi-band approach, uses a weighting scheme to emphasize the speech features in low frequency subbands. The cognitive load classification accuracy of this approach is shown to be higher than a system based on a non-weighting scheme. The second method is to design an effective filterbank based on the spectral distribution of cognitive load information using the Kullback-Leibler distance measure. It is shown that the designed filterbank consistently provides higher classification accuracies than other existing filterbanks such as mel, Bark, and equivalent rectangular bandwidth. A discrete cosine transform based speech enhancement technique is proposed in order to increase the robustness of the CL classification system and found to be more suitable than other methods investigated. This proposed method provides a 3.0% average relative error rate reduction for the seven types of noise and five levels of SNR used. In particular, it provides a maximum of 7.5% relative error rate reduction for the F16 noise (in NOISEX-92 database) at 20 dB SNR