Objective Measures of Cognitive Load Using Deep Multi-Modal Learning

The capability of measuring human performance objectively is hard to overstate, especially in the context of the instructor and student relationship within the process of learning. In this work, we investigate the automated classification of cognitive load leveraging the aviation domain as a surrogate for complex task workload induction. We use a mixed virtual and physical flight environment, given a suite of biometric sensors utilizing the HTC Vive Pro Eye and the E4 Empatica. We create and evaluate multiple models. And we have taken advantage of advancements in deep learning such as generative learning, multi-modal learning, multi-task learning, and x-vector architectures to classify multiple tasks across 40 subjects inclusive of three subject types --- pilots, operators, and novices. Our cognitive load model can automate the evaluation of cognitive load agnostic to subject, subject type, and flight maneuver (task) with an accuracy of over 80%. Further, this approach is validated with real-flight data from five test pilots collected over two test and evaluation flights on a C-17 aircraft.

Introducing phonetic information to speaker embedding for speaker verification

Phonetic information is one of the most essential components of a speech signal, playing an important role for many speech processing tasks. However, it is difficult to integrate phonetic information into speaker verification systems since it occurs primarily at the frame level while speaker characteristics typically reside at the segment level. In deep neural network-based speaker verification, existing methods only apply phonetic information to the frame-wise trained speaker embeddings. To improve this weakness, this paper proposes phonetic adaptation and hybrid multi-task learning and further combines these into c-vector and simplified c-vector architectures. Experiments on National Institute of Standards and Technology (NIST) speaker recognition evaluation (SRE) 2010 show that the four proposed speaker embeddings achieve better performance than the baseline. The c-vector system performs the best, providing over 30% and 15% relative improvements in equal error rate (EER) for the core-extended and 10 s–10 s conditions, respectively. On the NIST SRE 2016, 2018, and VoxCeleb datasets, the proposed c-vector approach improves the performance even when there is a language mismatch within the training sets or between the training and evaluation sets. Extensive experimental results demonstrate the effectiveness and robustness of the proposed methods.