The lack of a large, natural emotional database is one of the key barriers to translate results
on speech emotion recognition in controlled conditions into real-life applications. Collecting …

This study introduces a method to design a curriculum for machine-learning to maximize the
efficiency during the training process of deep neural networks (DNNs) for speech emotion …

Detection of human emotions is an essential part of affect-aware human-computer
interaction (HCI). In daily conversations, the preferred way of describing affects is by using …

A key element in affective computing is to have large corpora of genuine emotional samples
collected during natural conversations. Recording natural interactions through telephone is …

Abstract Machine learning can unintentionally encode and amplify negative bias and
stereotypes present in humans, be they conscious or unconscious. This has led to high …

Automatic recognition of emotions is an important part of affect-sensitive human-computer
interaction (HCI). Expressive behaviors tend to be ambiguous with blended emotions during …

A speech emotion retrieval system aims to detect a subset of data with specific expressive
content. Preference learning represents an appealing framework to rank speech samples in …

Preference learning is an appealing approach for affective recognition. Instead of predicting
the underlying emotional class of a sample, this framework relies on pairwise comparisons …

Daily living activity monitoring is important for early detection of the onset of many diseases
and for improving quality of life especially in elderly. A wireless wearable network of inertial …

Studies have shown that ranking emotional attributes through preference learning methods
has significant advantages over conventional emotional classification/regression …