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Found 11 papers, 3 papers with code
StatioCL: Contrastive Learning for Time Series via Non-Stationary and Temporal Contrast
However, existing CL methods often introduce false negative pairs (FNPs) by neglecting inherent characteristics and then randomly selecting distinct segments as dissimilar pairs, leading to erroneous representation learning, reduced model performance, and overall inefficiency.
AER-LLM: Ambiguity-aware Emotion Recognition Leveraging Large Language Models
Recent advancements in Large Language Models (LLMs) have demonstrated great success in many Natural Language Processing (NLP) tasks.
Efficient and Personalized Mobile Health Event Prediction via Small Language Models
Healthcare monitoring is crucial for early detection, timely intervention, and the ongoing management of health conditions, ultimately improving individuals' quality of life.
A Joint Spectro-Temporal Relational Thinking Based Acoustic Modeling Framework
Despite the crucial role relational thinking plays in human understanding of speech, it has yet to be leveraged in any artificial speech recognition systems.
Dual-Constrained Dynamical Neural ODEs for Ambiguity-aware Continuous Emotion Prediction
There has been a significant focus on modelling emotion ambiguity in recent years, with advancements made in representing emotions as distributions to capture ambiguity.
Speech Emotion Recognition Via CNN-Transformer and Multidimensional Attention Mechanism
In this paper, to model local and global information at different levels of granularity in speech and capture temporal, spatial and channel dependencies in speech signals, we propose a Speech Emotion Recognition network based on CNN-Transformer and multi-dimensional attention mechanisms.
Variational Connectionist Temporal Classification for Order-Preserving Sequence Modeling
Connectionist temporal classification (CTC) is commonly adopted for sequence modeling tasks like speech recognition, where it is necessary to preserve order between the input and target sequences.
Exploring Longitudinal Cough, Breath, and Voice Data for COVID-19 Progression Prediction via Sequential Deep Learning: Model Development and Validation
Recent work has shown the potential of using audio data (eg, cough, breathing, and voice) in the screening for COVID-19.
A Novel Markovian Framework for Integrating Absolute and Relative Ordinal Emotion Information
We propose a Markovian framework referred to as Dynamic Ordinal Markov Model (DOMM) that makes use of both absolute and relative ordinal information, to improve speech based ordinal emotion prediction.
Sounds of COVID-19: exploring realistic performance of audio-based digital testing
In this paper, we explore the realistic performance of audio-based digital testing of COVID-19.
Uncertainty-Aware COVID-19 Detection from Imbalanced Sound Data
To handle these issues, we propose an ensemble framework where multiple deep learning models for sound-based COVID-19 detection are developed from different but balanced subsets from original data.
