arXiv (All)
open access
Consensus-Guided Shared-Specific Tri-View Learning for Speech Emotion Recognition
Speech emotion recognition (SER) benefits from heterogeneous acoustic representations, but views derived from the same utterance contain both overlapping emotional evidence and representation-dependent cues. Direct fusion may therefore propagate redundant information or obscure complementary details
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Differential Attention Unlocks Complementary EEG and Speech Fusion for Emotion Recognition#1106098
- Dual-Scale State-Space Modeling with Speaker-Wise Dynamic CRF for Speech Emotion Recognition in Conversation#927434
- Subject-Invariant Cross-Modal Decoding of Perceived Speech from Brain Recordings#1105391
- Reading Emotions in the Token Space: Discriminative Adaptation of SpeechLLMs for Emotion Recognition#1033859
- Event segmentation during speech listening: The relative contributions of thematic structure and prosody#31115
Record · ID 1035648
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive