AI 中文总结
针对人工耳蜗语音情感识别线索被削弱的问题，提出无需并行录音或强度标签的SEER检索框架，在RAVDESS、ESD数据集及多条件下显著提升情感识别性能。
AI 中文摘要
人工耳蜗（CI）可恢复言语感知，但会削弱语音情感识别所需的线索。现有面向CI的增强方法需要并行的正常/强录音及强度标签。我们提出SEER，一种基于检索的框架，用于学习CI处理后哪种相同情感的参考能帮助每个源保持可识别性。源条件检索器从采样的情感语音转换结果中学习CI感知效用，同时不确定性引导探索避免穷尽配对评估；无需并行录音或强度标签。SEER在RAVDESS数据集上将N8下的源宏F1提升7.30个百分点，在ESD数据集上提升11.66个百分点，且ESD在N4/N8/N16下均有显著提升；16名听众的RAVDESS在所有条件下均有显著提升。穷尽分析发现，更强参考带来总体收益，但性别或内容匹配几乎无影响。
英文摘要
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.
CommentsSubmitted to ICASSP 2027. 5 pages, 2 figures, 3 tables. Code and demo audio: https://github.com/HenryChou36/SEER
