13 citations · 19 across the 5 of their papers we have counts for
5 papers
BidirLM: From Text to Omnimodal Bidirectional Encoders by Adapting and Composing Causal LLMs
Nicolas Boizard, Théo Deschamps-Berger, Hippolyte Gisserot-Boukhlef +2
Transforming causal generative language models into bidirectional encoders offers a powerful alternative to BERT-style architectures. However, current approaches remain limited: th…
A Multi-Task, Multi-Modal Approach for Predicting Categorical and Dimensional Emotions
Alex-Răzvan Ispas, Théo Deschamps-Berger, Laurence Devillers
Speech emotion recognition (SER) has received a great deal of attention in recent years in the context of spontaneous conversations. While there have been notable results on datase…
Multiscale Contextual Learning for Speech Emotion Recognition in Emergency Call Center Conversations
Théo Deschamps-Berger, Lori Lamel, Laurence Devillers
Emotion recognition in conversations is essential for ensuring advanced human-machine interactions. However, creating robust and accurate emotion recognition systems in real life i…
Exploring Attention Mechanisms for Multimodal Emotion Recognition in an Emergency Call Center Corpus
Théo Deschamps-Berger, Lori Lamel, Laurence Devillers
The emotion detection technology to enhance human decision-making is an important research issue for real-world applications, but real-life emotion datasets are relatively rare and…
End-to-End Speech Emotion Recognition: Challenges of Real-Life Emergency Call Centers Data Recordings
Théo Deschamps-Berger, Lori Lamel, Laurence Devillers
Recognizing a speaker's emotion from their speech can be a key element in emergency call centers. End-to-end deep learning systems for speech emotion recognition now achieve equiva…