The Successful Foreign Language Classroom: Affect, Empathy, and Engagement

This portfolio is the culmination of the author’s work in the Master of Second Language Teaching program at Utah State University. Throughout this collection the author shares her personal views and experiences from teaching secondary and postsecondary beginning Spanish courses, supporting her claims with existing research. The portfolio consists of three sections: (1) teaching perspectives, (2) research perspectives, and (3) annotated bibliographies. The objective of this work is to identify hindrances to the progress of foreign language teachers and learners, and best practices to stimulate their success. On the basis that language learning can promote cross-cultural understanding, these findings are valuable to educators and learners who seek to bridge cultural divides and unify their communities—one classroom at a time

Privacy Enhanced Multimodal Neural Representations for Emotion Recognition

Many mobile applications and virtual conversational agents now aim to recognize and adapt to emotions. To enable this, data are transmitted from users' devices and stored on central servers. Yet, these data contain sensitive information that could be used by mobile applications without user's consent or, maliciously, by an eavesdropping adversary. In this work, we show how multimodal representations trained for a primary task, here emotion recognition, can unintentionally leak demographic information, which could override a selected opt-out option by the user. We analyze how this leakage differs in representations obtained from textual, acoustic, and multimodal data. We use an adversarial learning paradigm to unlearn the private information present in a representation and investigate the effect of varying the strength of the adversarial component on the primary task and on the privacy metric, defined here as the inability of an attacker to predict specific demographic information. We evaluate this paradigm on multiple datasets and show that we can improve the privacy metric while not significantly impacting the performance on the primary task. To the best of our knowledge, this is the first work to analyze how the privacy metric differs across modalities and how multiple privacy concerns can be tackled while still maintaining performance on emotion recognition.Comment: 8 page

Best Practices for Noise-Based Augmentation to Improve the Performance of Deployable Speech-Based Emotion Recognition Systems

Speech emotion recognition is an important component of any human centered system. But speech characteristics produced and perceived by a person can be influenced by a multitude of reasons, both desirable such as emotion, and undesirable such as noise. To train robust emotion recognition models, we need a large, yet realistic data distribution, but emotion datasets are often small and hence are augmented with noise. Often noise augmentation makes one important assumption, that the prediction label should remain the same in presence or absence of noise, which is true for automatic speech recognition but not necessarily true for perception based tasks. In this paper we make three novel contributions. We validate through crowdsourcing that the presence of noise does change the annotation label and hence may alter the original ground truth label. We then show how disregarding this knowledge and assuming consistency in ground truth labels propagates to downstream evaluation of ML models, both for performance evaluation and robustness testing. We end the paper with a set of recommendations for noise augmentations in speech emotion recognition datasets