Executive Voiced Laughter and Social Approval: An Explorative Machine Learning Study

We study voiced laughter in executive communication and its effect on social approval. Integrating research on laughter, affect-as-information, and infomediaries' social evaluations of firms, we hypothesize that voiced laughter in executive communication positively affects social approval, defined as audience perceptions of affinity towards an organization. We surmise that the effect of laughter is especially strong for joint laughter, i.e., the number of instances in a given communication venue for which the focal executive and the audience laugh simultaneously. Finally, combining the notions of affect-as-information and negativity bias in human cognition, we hypothesize that the positive effect of laughter on social approval increases with bad organizational performance. We find partial support for our ideas when testing them on panel data comprising 902 German Bundesliga soccer press conferences and media tenor, applying state-of-the-art machine learning approaches for laughter detection as well as sentiment analysis. Our findings contribute to research at the nexus of executive communication, strategic leadership, and social evaluations, especially by introducing laughter as a highly consequential potential, but understudied social lubricant at the executive-infomediary interface. Our research is unique by focusing on reflexive microprocesses of social evaluations, rather than the infomediary-routines perspectives in infomediaries' evaluations. We also make methodological contributions.

Attention-Augmented End-to-End Multi-Task Learning for Emotion Prediction from Speech

Despite the increasing research interest in end-to-end learning systems for speech emotion recognition, conventional systems either suffer from the overfitting due in part to the limited training data, or do not explicitly consider the different contributions of automatically learnt representations for a specific task. In this contribution, we propose a novel end-to-end framework which is enhanced by learning other auxiliary tasks and an attention mechanism. That is, we jointly train an end-to-end network with several different but related emotion prediction tasks, i.e., arousal, valence, and dominance predictions, to extract more robust representations shared among various tasks than traditional systems with the hope that it is able to relieve the overfitting problem. Meanwhile, an attention layer is implemented on top of the layers for each task, with the aim to capture the contribution distribution of different segment parts for each individual task. To evaluate the effectiveness of the proposed system, we conducted a set of experiments on the widely used database IEMOCAP. The empirical results show that the proposed systems significantly outperform corresponding baseline systems.

Snore-GANs: Improving Automatic Snore Sound Classification with Synthesized Data

One of the frontier issues that severely hamper the development of automatic snore sound classification (ASSC) associates to the lack of sufficient supervised training data. To cope with this problem, we propose a novel data augmentation approach based on semi-supervised conditional Generative Adversarial Networks (scGANs), which aims to automatically learn a mapping strategy from a random noise space to original data distribution. The proposed approach has the capability of well synthesizing 'realistic' high-dimensional data, while requiring no additional annotation process. To handle the mode collapse problem of GANs, we further introduce an ensemble strategy to enhance the diversity of the generated data. The systematic experiments conducted on a widely used Munich-Passau snore sound corpus demonstrate that the scGANs-based systems can remarkably outperform other classic data augmentation systems, and are also competitive to other recently reported systems for ASSC.