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arXiv · 2610.11131

SEER: Source-Conditioned Emotion Enhancement via Retrieval for Cochlear-Implant Speech

Hsing-Hang Chou·Yun-Shao Lin·Ching-Chin Sung·Chi-Chun Lee
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Abstract

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 remain recognizable after CI processing. A source-conditioned retriever learns CI-aware utility from sampled emotional voice conversion outcomes, while uncertainty-guided exploration avoids exhaustive pair evaluation; neither parallel recordings nor intensity labels are required. SEER improves Source macro-F1 at N8 by 7.30 points on RAVDESS and 11.66 points on ESD, with significant ESD gains across N4/N8/N16. Sixteen-listener RAVDESS gains are significant across all conditions. Exhaustive analysis finds an aggregate benefit from stronger references but little effect from matching gender or content.

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Hsing-Hang Chou, Yun-Shao Lin, Ching-Chin Sung, Chi-Chun Lee. 2026-10-08. SEER: Source-Conditioned Emotion Enhancement via Retrieval for Cochlear-Implant Speech. https://arxiv.org/abs/2610.11131

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