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1. The MSP-Conversation Corpus

   https://doi.org/10.21437/Interspeech.2020-2444  

   Martinez-Lucas, Luz; Abdelwahab, Mohammed; Busso, Carlos (October 2020, Interspeech 2020)

   null (Ed.)

   Human-computer interactions can be very effective, especially if computers can automatically recognize the emotional state of the user. A key barrier for effective speech emotion recognition systems is the lack of large corpora annotated with emotional labels that reflect the temporal complexity of expressive behaviors, especially during multiparty interactions. This pa- per introduces the MSP-Conversation corpus, which contains interactions annotated with time-continuous emotional traces for arousal (calm to active), valence (negative to positive), and dominance (weak to strong). Time-continuous annotations offer the flexibility to explore emotional displays at different temporal resolutions while leveraging contextual information. This is an ongoing effort, where the corpus currently contains more than 15 hours of speech annotated by at least five annotators. The data is sourced from the MSP-Podcast corpus, which contains speech data from online audio-sharing websites annotated with sentence-level emotional scores. This data collection scheme is an easy, affordable, and scalable approach to obtain natural data with diverse emotional content from multiple speakers. This study describes the key features of the corpus. It also compares the time-continuous evaluations from the MSP- Conversation corpus with the sentence-level annotations of the MSP-Podcast corpus for the speech segments that overlap between the two corpora. 

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2. A Computational View of the Emotional Regulation of Disgust using Multimodal Sensors

   https://doi.org/10.1109/FG47880.2020.00145  

   Kota, Vidhathri; Gali, Geeta Madhav; Nwogu, Ifeoma (November 2020, 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020))

   null (Ed.)

   Emotion regulation can be characterized by different activities that attempt to alter an emotional response, whether behavioral, physiological or neurological. The two most widely adopted strategies, cognitive reappraisal and expressive suppression are explored in this study, specifically in the context of disgust. Study participants (N = 21) experienced disgust via video exposure, and were instructed to either regulate their emotions or express them freely. If regulating, they were required to either cognitively reappraise or suppress their emotional experiences while viewing the videos. Video recordings of the participants' faces were taken during the experiment and electrocardiogram (ECG), electromyography (EMG), and galvanic skin response (GSR) readings were also collected for further analysis. We compared the participants behavioral (facial musculature movements) and physiological (GSR and heart rate) responses as they aimed to alter their emotional responses and computationally determined that when responding to disgust stimuli, the signals recorded during suppression and free expression were very similar, whereas those recorded during cognitive reappraisal were significantly different. Thus, in the context of this study, from a signal analysis perspective, we conclude that emotion regulation via cognitive reappraisal significantly alters participants' physiological responses to disgust, unlike regulation via suppression. 

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3. Predicting Categorical Emotions by Jointly Learning Primary and Secondary Emotions through Multitask Learning

   https://doi.org/10.21437/Interspeech.2018-2464  

   Lotfian, Reza; Busso, Carlos (September 2018, Interspeech 2018)

   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 categorical emotion labels (e.g., sad, anger, surprise). In categorical emotion classification, multiple descriptors (with different degrees of relevance) can be assigned to a sample. Perceptual evaluations have relied on primary and secondary emotions to capture the ambiguous nature of spontaneous recordings. Primary emotion is the most relevant category felt by the evaluator. Secondary emotions capture other emotional cues also conveyed in the stimulus. In most cases, the labels collected from the secondary emotions are discarded, since assigning a single class label to a sample is preferred from an application perspective. In this work, we take advantage of both types of annotations to improve the performance of emotion classification. We collect the labels from all the annotations available for a sample and generate primary and secondary emotion labels. A classifier is then trained using multitask learning with both primary and secondary emotions. We experimentally show that considering secondary emotion labels during the learning process leads to relative improvements of 7.9% in F1-score for an 8-class emotion classification task. 

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4. A Comparative Analysis of Emotion-Detecting AI Systems with Respect to Algorithm Performance and Dataset Diversity

   https://doi.org/10.1145/3306618.3314284  

   Bryant, De'Aira; Howard, Ayanna (January 2019, Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society)

   In recent news, organizations have been considering the use of facial and emotion recognition for applications involving youth such as tackling surveillance and security in schools. However, the majority of efforts on facial emotion recognition research have focused on adults. Children, particularly in their early years, have been shown to express emotions quite differently than adults. Thus, before such algorithms are deployed in environments that impact the wellbeing and circumstance of youth, a careful examination should be made on their accuracy with respect to appropriateness for this target demographic. In this work, we utilize several datasets that contain facial expressions of children linked to their emotional state to evaluate eight different commercial emotion classification systems. We compare the ground truth labels provided by the respective datasets to the labels given with the highest confidence by the classification systems and assess the results in terms of matching score (TPR), positive predictive value, and failure to compute rate. Overall results show that the emotion recognition systems displayed subpar performance on the datasets of children's expressions compared to prior work with adult datasets and initial human ratings. We then identify limitations associated with automated recognition of emotions in children and provide suggestions on directions with enhancing recognition accuracy through data diversification, dataset accountability, and algorithmic regulation. 

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5. Dynamic Speech Emotion Recognition Using A Conditional Neural Process

   https://doi.org/10.1109/ICASSP48485.2024.10447805  

   Martinez-Lucas, Luz; Busso, Carlos (April 2024, IEEE)

   na (Ed.)

   The problem of predicting emotional attributes from speech has often focused on predicting a single value from a sentence or short speaking turn. These methods often ignore that natural emotions are both dynamic and dependent on context. To model the dynamic nature of emotions, we can treat the prediction of emotion from speech as a time-series problem. We refer to the problem of predicting these emotional traces as dynamic speech emotion recognition. Previous studies in this area have used models that treat all emotional traces as coming from the same underlying distribution. Since emotions are dependent on contextual information, these methods might obscure the context of an emotional interaction. This paper uses a neural process model with a segment-level speech emotion recognition (SER) model for this problem. This type of model leverages information from the time-series and predictions from the SER model to learn a prior that defines a distribution over emotional traces. Our proposed model performs 21% better than a bidirectional long short-term memory (BiLSTM) baseline when predicting emotional traces for valence. 

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