Automatically Measuring Emotion from Speech: New Methods to Move from the Lab to the Real World
Abstract
Emotion has intrigued researchers for generations. This fascination has permeated the engineering community, motivating the development of affective computing methods. However, human emotion remains notoriously difficult to accurately detect. As a result, emotion classification techniques are not always effective when deployed. This is a problem because we are missing out on the potential that emotion recognition provides: the opportunity to automatically measure an aspect of behavior that provides critical insight into our health and wellbeing, insight that is not always easily accessible. In this talk, I will discuss our efforts in developing multimodal emotion recognition approaches that are effective in natural environments and demonstrate how these approaches can be used to support mental health.