Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review
In recent years, the rapid advances in machine learning (ML) and information fusion has
made it possible to endow machines/computers with the ability of emotion understanding …
made it possible to endow machines/computers with the ability of emotion understanding …
[HTML][HTML] Lipopolysaccharide-induced model of neuroinflammation: mechanisms of action, research application and future directions for its use
A Skrzypczak-Wiercioch, K Sałat - Molecules, 2022 - mdpi.com
Despite advances in antimicrobial and anti-inflammatory therapies, inflammation and its
consequences still remain a significant problem in medicine. Acute inflammatory responses …
consequences still remain a significant problem in medicine. Acute inflammatory responses …
Ferv39k: A large-scale multi-scene dataset for facial expression recognition in videos
Current benchmarks for facial expression recognition (FER) mainly focus on static images,
while there are limited datasets for FER in videos. It is still ambiguous to evaluate whether …
while there are limited datasets for FER in videos. It is still ambiguous to evaluate whether …
The Geneva minimalistic acoustic parameter set (GeMAPS) for voice research and affective computing
Work on voice sciences over recent decades has led to a proliferation of acoustic
parameters that are used quite selectively and are not always extracted in a similar fashion …
parameters that are used quite selectively and are not always extracted in a similar fashion …
Speech emotion classification using attention-based LSTM
Automatic speech emotion recognition has been a research hotspot in the field of human-
computer interaction over the past decade. However, due to the lack of research on the …
computer interaction over the past decade. However, due to the lack of research on the …
Emotion recognition in speech using cross-modal transfer in the wild
Obtaining large, human labelled speech datasets to train models for emotion recognition is a
notoriously challenging task, hindered by annotation cost and label ambiguity. In this work …
notoriously challenging task, hindered by annotation cost and label ambiguity. In this work …
[HTML][HTML] Cross corpus multi-lingual speech emotion recognition using ensemble learning
Receiving an accurate emotional response from robots has been a challenging task for
researchers for the past few years. With the advancements in technology, robots like service …
researchers for the past few years. With the advancements in technology, robots like service …
Revisiting disentanglement and fusion on modality and context in conversational multimodal emotion recognition
It has been a hot research topic to enable machines to understand human emotions in
multimodal contexts under dialogue scenarios, which is tasked with multimodal emotion …
multimodal contexts under dialogue scenarios, which is tasked with multimodal emotion …
Features and classifiers for emotion recognition from speech: a survey from 2000 to 2011
CN Anagnostopoulos, T Iliou, I Giannoukos - Artificial Intelligence Review, 2015 - Springer
Speaker emotion recognition is achieved through processing methods that include isolation
of the speech signal and extraction of selected features for the final classification. In terms of …
of the speech signal and extraction of selected features for the final classification. In terms of …
[HTML][HTML] Mobile detection of autism through machine learning on home video: A development and prospective validation study
Background The standard approaches to diagnosing autism spectrum disorder (ASD)
evaluate between 20 and 100 behaviors and take several hours to complete. This has in …
evaluate between 20 and 100 behaviors and take several hours to complete. This has in …