arXiv:2505.11051
CAMEO: Collection of Multilingual Emotional Speech Corpora
원문 초록
This paper presents CAMEO -- a curated collection of multilingual emotional speech datasets designed to facilitate research in emotion recognition and other speech-related tasks. The main objectives were to ensure easy access to the data, to allow reproducibility of the results, and to provide a standardized benchmark for evaluating speech emotion recognition (SER) systems across different emotional states and languages. The paper describes the dataset selection criteria, the curation and normalization process, and provides performance results for several models. The collection, along with metadata, and a leaderboard, is publicly available via the Hugging Face platform.
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Dynamic Parameter Memory: Temporary LoRA-Enhanced LLM for Long-Sequence Emotion Recognition in Conversation
MEDUSA: A Multimodal Deep Fusion Multi-Stage Training Framework for Speech Emotion Recognition in Naturalistic Conditions
Multi-Teacher Language-Aware Knowledge Distillation for Multilingual Speech Emotion Recognition
MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge
Improving Speech Emotion Recognition Through Cross Modal Attention Alignment and Balanced Stacking Model
CosyVoice 3: Towards In-the-wild Speech Generation via Scaling-up and Post-training
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