  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.
