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Found 7 papers, 3 papers with code
A knowledge-driven vowel-based approach of depression classification from speech using data augmentation
Following that, the depression is classified at the global-level from a group of vowel CNN embeddings that serve as the input of another 1D CNN ("depression CNN").
A few-shot learning approach with domain adaptation for personalized real-life stress detection in close relationships
The proposed metric learning is based on a Siamese neural network (SNN) that learns the relative difference between pairs of samples from a target user and non-target users, thus being able to address the scarcity of labelled data from the target.
Toward Knowledge-Driven Speech-Based Models of Depression: Leveraging Spectrotemporal Variations in Speech Vowels
Explainability of the high-level information capturing the segment-by-segment decisions is further inspected for participants with and without depression.
Predicting the meal macronutrient composition from continuous glucose monitors
We built a multitask neural network to estimate the macronutrient composition from the CGM signal, and compared it against a baseline linear regression.
Few-shot Learning in Emotion Recognition of Spontaneous Speech Using a Siamese Neural Network with Adaptive Sample Pair Formation
Leveraging the abundance of labelled speech data from acted emotions, this paper proposes a few-shot learning approach for automatically recognizing emotion in spontaneous speech from a small number of labelled samples.
A Siamese Neural Network with Modified Distance Loss For Transfer Learning in Speech Emotion Recognition
Our system use samples from the source data to pre-train the weights of proposed Siamese neural network, which are fine-tuned based on the target data.
An adversarial learning framework for preserving users' anonymity in face-based emotion recognition
Image and video-capturing technologies have permeated our every-day life.
