Automatic speaker recognition under adverse conditions

Speaker verification is the process of verifying the identity of a person by analysing their speech. There are several important applications for automatic speaker verification (ASV) technology including suspect identification, tracking terrorists and detecting a person’s presence at a remote location in the surveillance domain, as well as person authentication for phone banking and credit card transactions in the private sector. Telephones and telephony networks provide a natural medium for these applications. The aim of this work is to improve the usefulness of ASV technology for practical applications in the presence of adverse conditions. In a telephony environment, background noise, handset mismatch, channel distortions, room acoustics and restrictions on the available testing and training data are common sources of errors for ASV systems. Two research themes were pursued to overcome these adverse conditions: Modelling mismatch and modelling uncertainty. To directly address the performance degradation incurred through mismatched conditions it was proposed to directly model this mismatch. Feature mapping was evaluated for combating handset mismatch and was extended through the use of a blind clustering algorithm to remove the need for accurate handset labels for the training data. Mismatch modelling was then generalised by explicitly modelling the session conditions as a constrained offset of the speaker model means. This session variability modelling approach enabled the modelling of arbitrary sources of mismatch, including handset type, and halved the error rates in many cases. Methods to model the uncertainty in speaker model estimates and verification scores were developed to address the difficulties of limited training and testing data. The Bayes factor was introduced to account for the uncertainty of the speaker model estimates in testing by applying Bayesian theory to the verification criterion, with improved performance in matched conditions. Modelling the uncertainty in the verification score itself met with significant success. Estimating a confidence interval for the "true" verification score enabled an order of magnitude reduction in the average quantity of speech required to make a confident verification decision based on a threshold. The confidence measures developed in this work may also have significant applications for forensic speaker verification tasks.

Discrete choice modelling of combined mode and departure time

Typical daily decision-making process of individuals regarding use of transport system involves mainly three types of decisions: mode choice, departure time choice and route choice. This paper focuses on the mode and departure time choice processes and studies different model specifications for a combined mode and departure time choice model. The paper compares different sets of explanatory variables as well as different model structures to capture the correlation among alternatives and taste variations among the commuters. The main hypothesis tested in this paper is that departure time alternatives are also correlated by the amount of delay. Correlation among different alternatives is confirmed by analyzing different nesting structures as well as error component formulations. Random coefficient logit models confirm the presence of the random taste heterogeneity across commuters. Mixed nested logit models are estimated to jointly account for the random taste heterogeneity and the correlation among different alternatives. Results indicate that accounting for the random taste heterogeneity as well as inter-alternative correlation improves the model performance.

Implementing value management as a decision-making tool in the design stages of design and build construction projects : a methodology for improved cost optimization

Value Management (VM) has been proven to provide a structured framework, together with other supporting tools and techniques, that facilitate effective decision-making in many types of projects, thus achieving ‘best value’ for clients. One of the major success factors of VM in achieving better project objectives for clients is through the provision of beneficial input by multi-disciplinary team members being involved in critical decision-making discussions during the early stage of construction projects. This paper describes a doctoral research proposal based on the application of VM in design and build construction projects, especially focusing on the design stage. The research aims to study the effects of implementing VM in design and build construction projects, in particular how well the methodology addresses issues related to cost overruns resulting from poor coordination and overlooking of critical constructability issues amongst team members in construction projects in Malaysia. It is proposed that through contractors’ early involvement during the design stage, combined with the use of the VM methodology, particularly as a decision-making tool, better optimization of construction cost can be achieved, thus promoting more efficient and effective constructability. The main methods used in this research involve a thorough literature study, semi-structured interviews, and a survey of major stakeholders, a detailed case study and a VM workshop and focus group discussions involving construction professionals in order to explore and possibly develop a framework and a specific methodology for the facilitating successful application of VM within design and build construction projects.

Best practices in prediction for decision-making : lessons from the atmospheric and earth sciences

Predictions that result from scientific research hold great appeal for decision-makers who are grappling with complex and controversial environmental issues, by promising to enhance their ability to determine a need for and outcomes of alternative decisions. A problem exists in that decision-makers and scientists in the public and private sectors solicit, produce, and use such predictions with little understanding of their accuracy or utility, and often without systematic evaluation or mechanisms of accountability. In order to contribute to a more effective role for ecological science in support of decision-making, this paper discusses three ``best practices'' for quantitative ecosystem modeling and prediction gleaned from research on modeling, prediction, and decision-making in the atmospheric and earth sciences. The lessons are distilled from a series of case studies and placed into the specific context of examples from ecological science.