We provide a short review on the recent and near-future developments of computational processing of emotion in the voice, highlighting (a) self-learning of representations moving continuously away from traditional expert-crafted or brute-forced feature representations to end-to-end learning, (b) a movement towards the coupling of analysis and synthesis of emotional voices to foster better mutual understanding, (c) weakly supervised learning at a large scale, (d) transfer learning from related domains such as speech recognition or cross-modal transfer learning, and (e) reinforced learning (...) through interactive applications at a large scale. For each of these trends, we shortly explain their implications and potential use such as for interpretation in psychological studies and usage in digital health and digital psychology applications. We also discuss further potential development. (shrink)

This report documents the program and the outcomes of Dagstuhl Seminar 21381 "Conversational Agent as Trustworthy Autonomous System ". First, we present the abstracts of the talks delivered by the Seminar’s attendees. Then we report on the origin and process of our six breakout groups. For each group, we describe its contributors, goals and key questions, key insights, and future research. The themes of the groups were derived from a pre-Seminar survey, which also led to a list of suggested readings (...) for the topic of trust in conversational agents. The list is included in this report for references. (shrink)

Purpose Uncertainty is an under-respected issue when it comes to automatic assessment of human emotion by machines. The purpose of this paper is to highlight the existent approaches towards such measurement of uncertainty, and identify further research need. Design/methodology/approach The discussion is based on a literature review. Findings Technical solutions towards measurement of uncertainty in automatic emotion recognition exist but need to be extended to respect a range of so far underrepresented sources of uncertainty. These then need to be integrated (...) into systems available to general users. Research limitations/implications Not all sources of uncertainty in automatic emotion recognition including emotion representation and annotation can be touched upon in this communication. Practical implications AER systems shall be enhanced by more meaningful and complete information provision on the uncertainty underlying their estimates. Limitations of their applicability should be communicated to users. Social implications Users of automatic emotion recognition technology will become aware of their limitations, potentially leading to a fairer usage in crucial application context. Originality/value There is no previous discussion including the technical view point on extended uncertainty measurement in automatic emotion recognition. (shrink)