[HTML][HTML] Survey on bimodal speech emotion recognition from acoustic and linguistic information fusion
Speech emotion recognition (SER) is traditionally performed using merely acoustic
information. Acoustic features, commonly are extracted per frame, are mapped into emotion …
information. Acoustic features, commonly are extracted per frame, are mapped into emotion …
[HTML][HTML] Interaction coding in leadership research: A critical review and best-practice recommendations to measure behavior
AV Güntner, AL Meinecke, ZEK Lüders - The Leadership Quarterly, 2023 - Elsevier
Leadership scholars increasingly acknowledge the shortcomings of using questionnaires.
Consequently, there is a trend towards more behavior-based research, with interaction …
Consequently, there is a trend towards more behavior-based research, with interaction …
[HTML][HTML] Conversational memory network for emotion recognition in dyadic dialogue videos
Emotion recognition in conversations is crucial for the development of empathetic machines.
Present methods mostly ignore the role of inter-speaker dependency relations while …
Present methods mostly ignore the role of inter-speaker dependency relations while …
All-in-One: Emotion, Sentiment and Intensity Prediction Using a Multi-Task Ensemble Framework
We propose a multi-task ensemble framework that jointly learns multiple related problems.
The ensemble model aims to leverage the learned representations of three deep learning …
The ensemble model aims to leverage the learned representations of three deep learning …
Improving cross-corpus speech emotion recognition with adversarial discriminative domain generalization (ADDoG)
J Gideon, MG McInnis… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Automatic speech emotion recognition provides computers with critical context to enable
user understanding. While methods trained and tested within the same dataset have been …
user understanding. While methods trained and tested within the same dataset have been …
Privacy enhanced multimodal neural representations for emotion recognition
M Jaiswal, EM Provost - Proceedings of the AAAI Conference on …, 2020 - ojs.aaai.org
Many mobile applications and virtual conversational agents now aim to recognize and adapt
to emotions. To enable this, data are transmitted from users' devices and stored on central …
to emotions. To enable this, data are transmitted from users' devices and stored on central …
Compact graph architecture for speech emotion recognition
We propose a deep graph approach to address the task of speech emotion recognition. A
compact, efficient and scalable way to represent data is in the form of graphs. Following the …
compact, efficient and scalable way to represent data is in the form of graphs. Following the …
Self-ensembling attention networks: Addressing domain shift for semantic segmentation
Recent years have witnessed the great success of deep learning models in semantic
segmentation. Nevertheless, these models may not generalize well to unseen image …
segmentation. Nevertheless, these models may not generalize well to unseen image …
Two-stage dimensional emotion recognition by fusing predictions of acoustic and text networks using SVM
Automatic speech emotion recognition (SER) by a computer is a critical component for more
natural human-machine interaction. As in human-human interaction, the capability to …
natural human-machine interaction. As in human-human interaction, the capability to …
Group gated fusion on attention-based bidirectional alignment for multimodal emotion recognition
Emotion recognition is a challenging and actively-studied research area that plays a critical
role in emotion-aware human-computer interaction systems. In a multimodal setting …
role in emotion-aware human-computer interaction systems. In a multimodal setting …