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Found 11 papers, 1 papers with code
STAA-Net: A Sparse and Transferable Adversarial Attack for Speech Emotion Recognition
Speech contains rich information on the emotions of humans, and Speech Emotion Recognition (SER) has been an important topic in the area of human-computer interaction.
Customising General Large Language Models for Specialised Emotion Recognition Tasks
This indicates the strong transferability and feasibility of LLMs in the field of emotion recognition.
Refashioning Emotion Recognition Modelling: The Advent of Generalised Large Models
After the inception of emotion recognition or affective computing, it has increasingly become an active research topic due to its broad applications.
Dynamic Restrained Uncertainty Weighting Loss for Multitask Learning of Vocal Expression
We propose a novel Dynamic Restrained Uncertainty Weighting Loss to experimentally handle the problem of balancing the contributions of multiple tasks on the ICML ExVo 2022 Challenge.
An Early Study on Intelligent Analysis of Speech under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety
In particular, by analysing speech recordings from these patients, we construct audio-only-based models to automatically categorise the health state of patients from four aspects, including the severity of illness, sleep quality, fatigue, and anxiety.
EmoBed: Strengthening Monomodal Emotion Recognition via Training with Crossmodal Emotion Embeddings
Motivated by this, we propose a novel crossmodal emotion embedding framework called EmoBed, which aims to leverage the knowledge from other auxiliary modalities to improve the performance of an emotion recognition system at hand.
Snore-GANs: Improving Automatic Snore Sound Classification with Synthesized Data
One of the frontier issues that severely hamper the development of automatic snore sound classification (ASSC) associates to the lack of sufficient supervised training data.
Attention-Augmented End-to-End Multi-Task Learning for Emotion Prediction from Speech
Despite the increasing research interest in end-to-end learning systems for speech emotion recognition, conventional systems either suffer from the overfitting due in part to the limited training data, or do not explicitly consider the different contributions of automatically learnt representations for a specific task.
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives
Over the past few years, adversarial training has become an extremely active research topic and has been successfully applied to various Artificial Intelligence (AI) domains.
Learning audio sequence representations for acoustic event classification
Extensive evaluation on a large-size acoustic event database is performed, and the empirical results demonstrate that the learnt audio sequence representation yields a significant performance improvement by a large margin compared with other state-of-the-art hand-crafted sequence features for AEC.
Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments
Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition that stills remains an important challenge.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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