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Found 5 papers, 2 papers with code
Investigating salient representations and label Variance in Dimensional Speech Emotion Analysis
Representations derived from models such as BERT (Bidirectional Encoder Representations from Transformers) and HuBERT (Hidden units BERT), have helped to achieve state-of-the-art performance in dimensional speech emotion recognition.
Frequency-Aware Masked Autoencoders for Multimodal Pretraining on Biosignals
To achieve effective pretraining in the presence of potential distributional shifts, we propose a frequency-aware masked autoencoder ($\texttt{bio}$FAME) that learns to parameterize the representation of biosignals in the frequency space.
Pre-trained Model Representations and their Robustness against Noise for Speech Emotion Analysis
We investigated the use of pre-trained model representations for estimating dimensional emotions, such as activation, valence, and dominance, from speech.
Speech Emotion: Investigating Model Representations, Multi-Task Learning and Knowledge Distillation
We investigate the use of pre-trained model representations to improve valence estimation from acoustic speech signal.
Subject-Aware Contrastive Learning for Biosignals
Datasets for biosignals, such as electroencephalogram (EEG) and electrocardiogram (ECG), often have noisy labels and have limited number of subjects (<100).
