The I4U Mega Fusion and Collaboration for NIST Speaker Recognition Evaluation 2016

The 2016 speaker recognition evaluation (SRE'16) is the latest edition in the series of benchmarking events conducted by the National Institute of Standards and Technology (NIST). I4U is a joint entry to SRE'16 as the result from the collaboration and active exchange of information among researchers from sixteen Institutes and Universities across 4 continents. The joint submission and several of its 32 sub-systems were among top-performing systems. A lot of efforts have been devoted to two major challenges, namely, unlabeled training data and dataset shift from Switchboard-Mixer to the new Call My Net dataset. This paper summarizes the lessons learned, presents our shared view from the sixteen research groups on recent advances, major paradigm shift, and common tool chain used in speaker recognition as we have witnessed in SRE'16. More importantly, we look into the intriguing question of fusing a large ensemble of sub-systems and the potential benefit of large-scale collaboration.Peer reviewe

Co-creation with Companies: A Means to Enhance Societal Impact of University Researchers?

In this chapter, we explore co-creation as a form of societal interaction of science. We approach co-creation as a goal-oriented form of dynamic interaction aiming at mutual benefit of all parties. As such, we exclude technology transfer and other linear societal interaction forms that follow a closed-model innovation format. We argue that focusing solely on tapping the needs of researchers and ‘pure’ science would lead to ignoring the broader context in which researchers work. An excessive focus on meeting the needs of external stakeholders could jeopardize the preconditions of science. Hence, this chapter explores how researcher-company co-creation can be nurtured in a heavily institutionalized setting, where established rules govern the process of knowledge production and protect research integrity. The co-creation process is analyzed by combining Nonaka’s SECI model and Strober’s interdisciplinary interaction model for knowledge creation. We find that the core of this process lies facilitated dialogue, which is seen as open knowledge sharing between equal participants

How to construct perfect and worse-than-coin-flip spoofing countermeasures:a word of warning on shortcut learning

Abstract Shortcut learning, or ‘Clever Hans effect’ refers to situations where a learning agent (e.g., deep neural networks) learns spurious correlations present in data, resulting in biased models. We focus on finding shortcuts in deep learning based spoofing countermeasures (CMs) that predict whether a given utterance is spoofed or not. While prior work has addressed specific data artifacts, such as silence, no general normative framework has been explored for analyzing shortcut learning in CMs. In this study, we propose a generic approach to identifying shortcuts by introducing systematic interventions on the training and test sides, including the boundary cases of ‘near-perfect’ and ‘worse than coin flip’ (label flip). By using three different models, ranging from classic to state-of-the-art, we demonstrate the presence of shortcut learning in five simulated conditions. We also analyze the results using a regression model to understand how biases affect the class-conditional score statistics