Human and Machine Speaker Recognition Based on Short Trivial Events

Trivial events are ubiquitous in human to human conversations, e.g., cough, laugh and sniff. Compared to regular speech, these trivial events are usually short and unclear, thus generally regarded as not speaker discriminative and so are largely ignored by present speaker recognition research. However, these trivial events are highly valuable in some particular circumstances such as forensic examination, as they are less subjected to intentional change, so can be used to discover the genuine speaker from disguised speech. In this paper, we collect a trivial event speech database that involves 75 speakers and 6 types of events, and report preliminary speaker recognition results on this database, by both human listeners and machines. Particularly, the deep feature learning technique recently proposed by our group is utilized to analyze and recognize the trivial events, which leads to acceptable equal error rates (EERs) despite the extremely short durations (0.2-0.5 seconds) of these events. Comparing different types of events, 'hmm' seems more speaker discriminative.Comment: ICASSP 201

Taking Turing by Surprise? Designing Digital Computers for morally-loaded contexts

There is much to learn from what Turing hastily dismissed as Lady Lovelace s objection. Digital computers can indeed surprise us. Just like a piece of art, algorithms can be designed in such a way as to lead us to question our understanding of the world, or our place within it. Some humans do lose the capacity to be surprised in that way. It might be fear, or it might be the comfort of ideological certainties. As lazy normative animals, we do need to be able to rely on authorities to simplify our reasoning: that is ok. Yet the growing sophistication of systems designed to free us from the constraints of normative engagement may take us past a point of no-return. What if, through lack of normative exercise, our moral muscles became so atrophied as to leave us unable to question our social practices? This paper makes two distinct normative claims: 1. Decision-support systems should be designed with a view to regularly jolting us out of our moral torpor. 2. Without the depth of habit to somatically anchor model certainty, a computer s experience of something new is very different from that which in humans gives rise to non-trivial surprises. This asymmetry has key repercussions when it comes to the shape of ethical agency in artificial moral agents. The worry is not just that they would be likely to leap morally ahead of us, unencumbered by habits. The main reason to doubt that the moral trajectories of humans v. autonomous systems might remain compatible stems from the asymmetry in the mechanisms underlying moral change. Whereas in humans surprises will continue to play an important role in waking us to the need for moral change, cognitive processes will rule when it comes to machines. This asymmetry will translate into increasingly different moral outlooks, to the point of likely unintelligibility. The latter prospect is enough to doubt the desirability of autonomous moral agents

Deferred Action: Theoretical model of process architecture design for emergent business processes

E-Business modelling and ebusiness systems development assumes fixed company resources, structures, and business processes. Empirical and theoretical evidence suggests that company resources and structures are emergent rather than fixed. Planning business activity in emergent contexts requires flexible ebusiness models based on better management theories and models . This paper builds and proposes a theoretical model of ebusiness systems capable of catering for emergent factors that affect business processes. Drawing on development of theories of the ‘action and design’class the Theory of Deferred Action is invoked as the base theory for the theoretical model. A theoretical model of flexible process architecture is presented by identifying its core components and their relationships, and then illustrated with exemplar flexible process architectures capable of responding to emergent factors. Managerial implications of the model are considered and the model’s generic applicability is discussed

Identifying immersive environments’ most relevant research topics: an instrument to query researchers and practitioners

This paper provides an instrument for ascertaining researchers’ perspectives on the relative relevance of technological challenges facing immersive environments in view of their adoption in learning contexts, along three dimensions: access, content production, and deployment. It described its theoretical grounding and expert-review process, from a set of previously-identified challenges and expert feedback cycles. The paper details the motivation, setup, and methods employed, as well as the issues detected in the cycles and how they were addressed while developing the instrument. As a research instrument, it aims to be employed across diverse communities of research and practice, helping direct research efforts and hence contribute to wider use of immersive environments in learning, and possibly contribute towards the development of news and more adequate systems.The work presented herein has been partially funded under the European H2020 program H2020-ICT-2015, BEACONING project, grant agreement nr. 687676.info:eu-repo/semantics/publishedVersio

Formal Verification of Input-Output Mappings of Tree Ensembles

Recent advances in machine learning and artificial intelligence are now being considered in safety-critical autonomous systems where software defects may cause severe harm to humans and the environment. Design organizations in these domains are currently unable to provide convincing arguments that their systems are safe to operate when machine learning algorithms are used to implement their software. In this paper, we present an efficient method to extract equivalence classes from decision trees and tree ensembles, and to formally verify that their input-output mappings comply with requirements. The idea is that, given that safety requirements can be traced to desirable properties on system input-output patterns, we can use positive verification outcomes in safety arguments. This paper presents the implementation of the method in the tool VoTE (Verifier of Tree Ensembles), and evaluates its scalability on two case studies presented in current literature. We demonstrate that our method is practical for tree ensembles trained on low-dimensional data with up to 25 decision trees and tree depths of up to 20. Our work also studies the limitations of the method with high-dimensional data and preliminarily investigates the trade-off between large number of trees and time taken for verification