In the fulfilment of the DoA, the experienced researcher (ER) has carried out ground research towards holistic machine perception of human phenomena as a whole, using deep learning models to jointly recognize emotion, age, illness, and many other user attributes. In her work on holistic affect recognition, the ER has successfully brought together the field of Computational Paralinguistics (her PhD background) and Affective Computing (expertise of the host institution).
The major scientific achievement in the outgoing period is holistic affect recognition using deep learning techniques, which implements two main aspects of the HOL-DEEP-SENSE project. In the following, the research outputs are described.
In Affective Computing, research has aimed at endowing machines with emotional intelligence, which should support collaboration and interaction with human beings. Despite the performance achievements of today's systems, they are mostly designed to recognize emotion alone. However, considerations in both neuroscience and psychology have identified contextual cues as playing a central role in human perception of other people's emotion; we attend to individual differences in emotion expression, which are due to personal factors and social influence. Hence, analysing contextual information conveyed in a speaker's voice helps personalize emotion recognition technologies, which will enable emotionally and socially intelligent conversational AI.
In the published paper "PaNDA: Paralinguistic Non-metric Dimensional Analysis for Holistic Affect Recognition", a total of 18 speaker attributes are learned together: Negative emotion, age, interest, intoxication, sleepiness, Big-five personality traits (Openness, Conscientiousness, Extroversion, Agreeableness, Neuroticism), conflict, arousal, valence, cognitive load, physical load, sincerity, cold, and stress. Detailed explanation of the approach and discussion of results can be found in the open access publication of the paper (
https://dspace.mit.edu/handle/1721.1/123806(s’ouvre dans une nouvelle fenêtre)) as well as on the project webpage (
https://www.media.mit.edu/projects/hol-deep-sense/overview/(s’ouvre dans une nouvelle fenêtre)).
Another major achievement during the project phase is the development of machine learning methods for efficient data annotation, leading to one journal published in the IEEE Transactions on Cybernetics ("A Generic Human-Machine Annotation Framework Based on Dynamic Cooperative Learning") and another journal submitted to the Journal of Machine Learning Research opensource track. The new software will be made publically available for the research community upon publication and is broadly applicable for machine learning and data mining tasks that require multi-target training sets. It also provides the first open-source implementation of a multi-task shared hidden layer DNN capable of handling missing labels, based on the work presented above.
