arXiv (All)
open access
SEER: Source-Conditioned Emotion Enhancement via Retrieval for Cochlear-Implant Speech
Cochlear implants (CIs) restore speech access but weaken cues needed for vocal emotion recognition. Prior CI-oriented enhancement requires parallel normal/strong recordings and intensity labels. We propose SEER, a retrieval-based framework that learns which same-emotion reference helps each source r
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
- Reading Emotions in the Token Space: Discriminative Adaptation of SpeechLLMs for Emotion Recognition#1033859
- Transcript-Supervised Post-Training of Generative Speech Enhancement on Real Recordings via Reinforce Adjoint Matching#1077334
- Physiological Noise Augmentation Improves Non-Invasive Brain-to-Speech#343495
- Steerspeech: Activation Steering For Emotion Control In Generated Speech#1234840
- Steerspeech: Activation Steering For Emotion Control In Generated Speech#1227329
Record · ID 1248058
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