Introduces the eJSL Dialog dataset (1,920 videos in 480 dialogues from STUDIES corpus) for conversational sign language emotion recognition and benchmarks models revealing a domain gap with generic multimodal approaches.
A comprehensive review of multimodal emotion recognition: Techniques, challenges, and future directions
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
HQTN-SER combines a low-parameter quantum tensor network module with classical latent embeddings to reach 73-80% accuracy on three speech emotion datasets while using few qubits and showing stable training.
citing papers explorer
-
Emotion Recognition in Sign Language Conversation
Introduces the eJSL Dialog dataset (1,920 videos in 480 dialogues from STUDIES corpus) for conversational sign language emotion recognition and benchmarks models revealing a domain gap with generic multimodal approaches.
-
HQTN-SER: Speech Emotion Recognition with Hybrid Quantum Tensor Networks
HQTN-SER combines a low-parameter quantum tensor network module with classical latent embeddings to reach 73-80% accuracy on three speech emotion datasets while using few qubits and showing stable training.