arXiv (All)
open access
Reading Emotions in the Token Space: Discriminative Adaptation of SpeechLLMs for Emotion Recognition
SpeechLLMs have shown strong potential for emotion recognition, yet they read the predicted emotion off a generative decoder not suited for classification: it can emit labels outside the target set and favors frequent classes. We propose a discriminative adaptation that reads the final prompt token'
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
- TargetSEC: Plug-and-Play In-the-Wild Speech Emotion Conversion via Arousal-Conditioned Latent Style Diffusion#1011126
- Differential Attention Unlocks Complementary EEG and Speech Fusion for Emotion Recognition#1106098
- Emotion expression in song and speech: Determining acoustic cues through supervised machine learning techniques#295808
- Context-aware tokenization for Cross-subject Emotion Decoding from EEG#1118586
- Ensemble of transformers for depression emotion classification.#13864
Record · ID 1033859
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