Speaker Recognition in Unconstrained Environments

Speaker recognition is applied in smart home devices, interactive voice response systems, call centers, online banking and payment solutions as well as in forensic scenarios. This dissertation is concerned with speaker recognition systems in unconstrained environments. Before this dissertation, research on making better decisions in unconstrained environments was insufficient. Aside from decision making, unconstrained environments imply two other subjects: security and privacy. Within the scope of this dissertation, these research subjects are regarded as both security against short-term replay attacks and privacy preservation within state-of-the-art biometric voice comparators in the light of a potential leak of biometric data. The aforementioned research subjects are united in this dissertation to sustain good decision making processes facing uncertainty from varying signal quality and to strengthen security as well as preserve privacy. Conventionally, biometric comparators are trained to classify between mated and non-mated reference,--,probe pairs under idealistic conditions but are expected to operate well in the real world. However, the more the voice signal quality degrades, the more erroneous decisions are made. The severity of their impact depends on the requirements of a biometric application. In this dissertation, quality estimates are proposed and employed for the purpose of making better decisions on average in a formalized way (quantitative method), while the specifications of decision requirements of a biometric application remain unknown. By using the Bayesian decision framework, the specification of application-depending decision requirements is formalized, outlining operating points: the decision thresholds. The assessed quality conditions combine ambient and biometric noise, both of which occurring in commercial as well as in forensic application scenarios. Dual-use (civil and governmental) technology is investigated. As it seems unfeasible to train systems for every possible signal degradation, a low amount of quality conditions is used. After examining the impact of degrading signal quality on biometric feature extraction, the extraction is assumed ideal in order to conduct a fair benchmark. This dissertation proposes and investigates methods for propagating information about quality to decision making. By employing quality estimates, a biometric system's output (comparison scores) is normalized in order to ensure that each score encodes the least-favorable decision trade-off in its value. Application development is segregated from requirement specification. Furthermore, class discrimination and score calibration performance is improved over all decision requirements for real world applications. In contrast to the ISOIEC 19795-1:2006 standard on biometric performance (error rates), this dissertation is based on biometric inference for probabilistic decision making (subject to prior probabilities and cost terms). This dissertation elaborates on the paradigm shift from requirements by error rates to requirements by beliefs in priors and costs. Binary decision error trade-off plots are proposed, interrelating error rates with prior and cost beliefs, i.e., formalized decision requirements. Verbal tags are introduced to summarize categories of least-favorable decisions: the plot's canvas follows from Bayesian decision theory. Empirical error rates are plotted, encoding categories of decision trade-offs by line styles. Performance is visualized in the latent decision subspace for evaluating empirical performance regarding changes in prior and cost based decision requirements. Security against short-term audio replay attacks (a collage of sound units such as phonemes and syllables) is strengthened. The unit-selection attack is posed by the ASVspoof 2015 challenge (English speech data), representing the most difficult to detect voice presentation attack of this challenge. In this dissertation, unit-selection attacks are created for German speech data, where support vector machine and Gaussian mixture model classifiers are trained to detect collage edges in speech representations based on wavelet and Fourier analyses. Competitive results are reached compared to the challenged submissions. Homomorphic encryption is proposed to preserve the privacy of biometric information in the case of database leakage. In this dissertation, log-likelihood ratio scores, representing biometric evidence objectively, are computed in the latent biometric subspace. Conventional comparators rely on the feature extraction to ideally represent biometric information, latent subspace comparators are trained to find ideal representations of the biometric information in voice reference and probe samples to be compared. Two protocols are proposed for the the two-covariance comparison model, a special case of probabilistic linear discriminant analysis. Log-likelihood ratio scores are computed in the encrypted domain based on encrypted representations of the biometric reference and probe. As a consequence, the biometric information conveyed in voice samples is, in contrast to many existing protection schemes, stored protected and without information loss. The first protocol preserves privacy of end-users, requiring one public/private key pair per biometric application. The latter protocol preserves privacy of end-users and comparator vendors with two key pairs. Comparators estimate the biometric evidence in the latent subspace, such that the subspace model requires data protection as well. In both protocols, log-likelihood ratio based decision making meets the requirements of the ISOIEC 24745:2011 biometric information protection standard in terms of unlinkability, irreversibility, and renewability properties of the protected voice data

Methods for Using Manpower to Assess USAF Strategic Risk

With limited personnel resource funding availability, senior US Air Force (USAF) decision makers struggle to base enterprise resource allocation from rigorous analytical traceability. There are over 240 career fields in the USAF spanning 12 enterprises. Each enterprise develops annual risk assessments by distinctive core capabilities. A core capability (e.g. Research and Development) is an enabling function necessary for the USAF to perform its mission as part of the Department of Defense (DOD). Assessing risk at the core capability is a good start to assessing risk, but is still not comprehensiveness enough. One of the twelve enterprises has linked its task structure to Program Element Codes (PECs). Planners and programmers use amount of funding per PEC to assess tasks needed to address a desired capability. For the first time, a linkage between core functions, core capabilities, PECs, tasks and manpower has been developed. We now can provide an objective nomenclatured way to compute personnel risk. All resources planned are not programmed (i.e. resource allocated and budgeted); the delta between the two translate into capability gaps and a level of strategic risk. A USAF career field risk demonstration is performed using normal, sigmoid and Euclidean-norm functions. Understanding potential personnel shortfalls at the career field level should better inform core capability analysis, and thus increase credibility and defensibility of strategic risk assessment

Optimal sensor placement for sewer capacity risk management

2019 Spring.Includes bibliographical references.Complex linear assets, such as those found in transportation and utilities, are vital to economies, and in some cases, to public health. Wastewater collection systems in the United States are vital to both. Yet effective approaches to remediating failures in these systems remains an unresolved shortfall for system operators. This shortfall is evident in the estimated 850 billion gallons of untreated sewage that escapes combined sewer pipes each year (US EPA 2004a) and the estimated 40,000 sanitary sewer overflows and 400,000 backups of untreated sewage into basements (US EPA 2001). Failures in wastewater collection systems can be prevented if they can be detected in time to apply intervention strategies such as pipe maintenance, repair, or rehabilitation. This is the essence of a risk management process. The International Council on Systems Engineering recommends that risks be prioritized as a function of severity and occurrence and that criteria be established for acceptable and unacceptable risks (INCOSE 2007). A significant impediment to applying generally accepted risk models to wastewater collection systems is the difficulty of quantifying risk likelihoods. These difficulties stem from the size and complexity of the systems, the lack of data and statistics characterizing the distribution of risk, the high cost of evaluating even a small number of components, and the lack of methods to quantify risk. This research investigates new methods to assess risk likelihood of failure through a novel approach to placement of sensors in wastewater collection systems. The hypothesis is that iterative movement of water level sensors, directed by a specialized metaheuristic search technique, can improve the efficiency of discovering locations of unacceptable risk. An agent-based simulation is constructed to validate the performance of this technique along with testing its sensitivity to varying environments. The results demonstrated that a multi-phase search strategy, with a varying number of sensors deployed in each phase, could efficiently discover locations of unacceptable risk that could be managed via a perpetual monitoring, analysis, and remediation process. A number of promising well-defined future research opportunities also emerged from the performance of this research

Dating Victorians: an experimental approach to stylochronometry

A thesis submitted for the degree of Doctor of Philosophy ofthe University of LutonThe writing style of a number of authors writing in English was empirically investigated for the purpose of detecting stylistic patterns in relation to advancing age. The aim was to identify the type of stylistic markers among lexical, syntactical, phonemic, entropic, character-based, and content ones that would be most able to discriminate between early, middle, and late works of the selected authors, and the best classification or prediction algorithm most suited for this task. Two pilot studies were initially conducted. The first one concentrated on Christina Georgina Rossetti and Edgar Allan Poe from whom personal letters and poetry were selected as the genres of study, along with a limited selection of variables. Results suggested that authors and genre vary inconsistently. The second pilot study was based on Shakespeare's plays using a wider selection of variables to assess their discriminating power in relation to a past study. It was observed that the selected variables were of satisfactory predictive power, hence judged suitable for the task. Subsequently, four experiments were conducted using the variables tested in the second pilot study and personal correspondence and poetry from two additional authors, Edna St Vincent Millay and William Butler Yeats. Stepwise multiple linear regression and regression trees were selected to deal with the first two prediction experiments, and ordinal logistic regression and artificial neural networks for two classification experiments. The first experiment revealed inconsistency in accuracy of prediction and total number of variables in the final models affected by differences in authorship and genre. The second experiment revealed inconsistencies for the same factors in terms of accuracy only. The third experiment showed total number of variables in the model and error in the final model to be affected in various degrees by authorship, genre, different variable types and order in which the variables had been calculated. The last experiment had all measurements affected by the four factors. Examination of whether differences in method within each task play an important part revealed significant influences of method, authorship, and genre for the prediction problems, whereas all factors including method and various interactions dominated in the classification problems. Given the current data and methods used, as well as the results obtained, generalizable conclusions for the wider author population have been avoided