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Found 4 papers, 1 papers with code
When to Intervene: Detecting Abnormal Mood using Everyday Smartphone Conversations
Predicting when to intervene is challenging because there is not a single measure that is relevant for every person: different individuals may have different levels of symptom severity considered typical.
Jointly Aligning and Predicting Continuous Emotion Annotations
To deal with this challenge, we introduce a new convolutional neural network (multi-delay sinc network) that is able to simultaneously align and predict labels in an end-to-end manner.
Improving Cross-Corpus Speech Emotion Recognition with Adversarial Discriminative Domain Generalization (ADDoG)
We also show how, in most cases, ADDoG and MADDoG can be used to improve upon baseline state-of-the-art methods when target dataset labels are added and in-the-wild data are considered.
Trainable Time Warping: Aligning Time-Series in the Continuous-Time Domain
We introduce trainable time warping (TTW), whose complexity is linear in both the number and the length of time-series.
