Speech-Based Emotion Recognition using Neural Networks and Information Visualization
This work addresses the need for user-friendly emotion recognition tools in therapy, though it appears incremental as it builds on existing models with a focus on visualization.
The authors tackled the problem of subjective and information-heavy emotion assessment in therapy by developing a tool that uses a machine learning model to classify eight emotions from speech samples, providing therapists with an intuitive dashboard for analysis.
Emotions recognition is commonly employed for health assessment. However, the typical metric for evaluation in therapy is based on patient-doctor appraisal. This process can fall into the issue of subjectivity, while also requiring healthcare professionals to deal with copious amounts of information. Thus, machine learning algorithms can be a useful tool for the classification of emotions. While several models have been developed in this domain, there is a lack of userfriendly representations of the emotion classification systems for therapy. We propose a tool which enables users to take speech samples and identify a range of emotions (happy, sad, angry, surprised, neutral, clam, disgust, and fear) from audio elements through a machine learning model. The dashboard is designed based on local therapists' needs for intuitive representations of speech data in order to gain insights and informative analyses of their sessions with their patients.