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Found 18 papers, 2 papers with code
Analysis of Behavior Classification in Motivational Interviewing
Analysis of client and therapist behavior in counseling sessions can provide helpful insights for assessing the quality of the session and consequently, the client’s behavioral outcome.
Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need
We leverage multilevel Monte Carlo (MLMC) to improve the performance of multi-step look-ahead Bayesian optimization (BO) methods that involve nested expectations and maximizations.
Improving Selective Visual Question Answering by Learning from Your Peers
In this work, we explore Selective VQA in both in-distribution (ID) and OOD scenarios, where models are presented with mixtures of ID and OOD data.
Importance-based Multimodal Autoencoder
In this paper we propose the IMA (Importance-based Multimodal Autoencoder) model, a scalable model that learns modality importances and robust multimodal representations through a novel cross-covariance based loss function.
Modeling Temporality of Human Intentions by Domain Adaptation
Categorizing patient{'}s intentions in conversational assessment can help decision making in clinical treatments.
What type of happiness are you looking for? - A closer look at detecting mental health from language
Computational models to detect mental illnesses from text and speech could enhance our understanding of mental health while offering opportunities for early detection and intervention.
A Linguistically-Informed Fusion Approach for Multimodal Depression Detection
Automated depression detection is inherently a multimodal problem.
A Cross-modal Review of Indicators for Depression Detection Systems
Automatic detection of depression has attracted increasing attention from researchers in psychology, computer science, linguistics, and related disciplines.
Learning Representations of Emotional Speech with Deep Convolutional Generative Adversarial Networks
Automatically assessing emotional valence in human speech has historically been a difficult task for machine learning algorithms.
Affect-LM: A Neural Language Model for Customizable Affective Text Generation
Human verbal communication includes affective messages which are conveyed through use of emotionally colored words.
AVEC 2016 - Depression, Mood, and Emotion Recognition Workshop and Challenge
The Audio/Visual Emotion Challenge and Workshop (AVEC 2016) "Depression, Mood and Emotion" will be the sixth competition event aimed at comparison of multimedia processing and machine learning methods for automatic audio, visual and physiological depression and emotion analysis, with all participants competing under strictly the same conditions.
A Multimodal Corpus for the Assessment of Public Speaking Ability and Anxiety
As such, tools enabling the improvement of public speaking performance and the assessment and mitigation of anxiety related to public speaking would be very useful.
Learning Representations of Affect from Speech
Experiments on a well-established real-life speech dataset (IEMOCAP) show that the learnt representations are comparable to state of the art feature extractors (such as voice quality features and MFCCs) and are competitive with state-of-the-art approaches at emotion and dimensional affect recognition.
The Distress Analysis Interview Corpus of human and computer interviews
The Distress Analysis Interview Corpus (DAIC) contains clinical interviews designed to support the diagnosis of psychological distress conditions such as anxiety, depression, and post traumatic stress disorder.
An audiovisual political speech analysis incorporating eye-tracking and perception data
Additionally, we compare the gaze behavior of the human subjects to evaluate saliency regions in the multimodal and visual only conditions.
