语调作为监督：连接非语言与语言以实现多语言语音情绪识别
Prosody as Supervision: Bridging the Non-Verbal--Verbal for Multilingual Speech Emotion Recognition
AI总结：
本文提出一种非语言到语言的迁移框架，通过非语言语音特征提升多语言语音情绪识别性能，采用超几何向量量化代码本和超几何情绪透镜，实现情绪强度建模，优于传统欧几里得方法。
摘要：
在本文中，我们介绍了一种参数化监督范式，用于低资源多语言语音情绪识别（LRM-SER），该范式利用非语言语音特征来利用以语调为中心的情绪线索。与传统SER系统依赖大量标记的语音并遭受跨语言迁移差不同，我们的方法将LRM-SER重新表述为非语言到语言的迁移，其中来自标记非语言源域的监督被适应到多个目标语言的未标记语音中。为此，我们提出了NOVA ARC，一个几何感知框架，它在庞加莱球上建模情感结构，通过超几何向量量化代码本离散化非语言模式，并通过超几何情绪透镜捕捉情绪强度。对于无监督适应，NOVA-ARC在源情绪原型和目标语音之间执行基于最优传输的原型对齐，诱导对未标记语音的软监督，同时通过一致性正则化稳定。实验表明，NOVA-ARC在非语言到语言适应和互补语言到语言迁移设置下均表现最佳，始终优于欧几里得方法和强SSL基线。据我们所知，本文是首次超越语音中心监督，引入非语言到语言迁移范式用于SER的工作。
英文摘要：
In this work, we introduce a paralinguistic supervision paradigm for low-resource multilingual speech emotion recognition (LRM-SER) that leverages non-verbal vocalizations to exploit prosody-centric emotion cues. Unlike conventional SER systems that rely heavily on labeled verbal speech and suffer from poor cross-lingual transfer, our approach reformulates LRM-SER as non-verbal-to-verbal transfer, where supervision from a labeled non-verbal source domain is adapted to unlabeled verbal speech across multiple target languages. To this end, we propose NOVA ARC, a geometry-aware framework that models affective structure in the Poincaré ball, discretizes paralinguistic patterns via a hyperbolic vector-quantized prosody codebook, and captures emotion intensity through a hyperbolic emotion lens. For unsupervised adaptation, NOVA-ARC performs optimal transport based prototype alignment between source emotion prototypes and target utterances, inducing soft supervision for unlabeled speech while being stabilized through consistency regularization. Experiments show that NOVA-ARC delivers the strongest performance under both non-verbal-to-verbal adaptation and the complementary verbal-to-verbal transfer setting, consistently outperforming Euclidean counterparts and strong SSL baselines. To the best of our knowledge, this work is the first to move beyond verbal-speech-centric supervision by introducing a non-verbal-to-verbal transfer paradigm for SER.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
