共识引导的共享-特定三视图学习用于语音情感识别
Consensus-Guided Shared-Specific Tri-View Learning for Speech Emotion Recognition
AI总结：
针对语音情感识别中多视图融合冗余与互补信息丢失问题，提出TriCGF方法，通过共识学习与门控集成建模语谱图、MFCC和HuBERT，在IEMOCAP和EmoDB上取得最优性能。
AI中文摘要：
语音情感识别（SER）受益于异构声学表征，但源自同一话语的视图既包含重叠的情感证据，也包含依赖于表征的线索。直接融合因此可能传播冗余信息或掩盖互补细节。为解决此问题，我们提出三视图共识引导融合（TriCGF），用于联合建模语谱图、梅尔频率倒谱系数和HuBERT表征。TriCGF在融合前将每个视图组织为公共组件和视图特定组件。跨视图共识学习将公共组件聚合为全局参考，而视图门控集成自适应地将该参考与每个视图特定组件结合。软差异正则化进一步抑制过度的信息重叠。在说话人独立评估下，TriCGF在IEMOCAP上达到74.19%的加权准确率（WA）和75.17%的未加权准确率（UA），在EmoDB上达到94.36%的WA和94.28%的UA，在两个数据集上均优于代表性的SER方法。
英文摘要：
Speech emotion recognition (SER) benefits from heterogeneous acoustic representations, but views derived from the same utterance contain both overlapping emotional evidence and representation-dependent cues. Direct fusion may therefore propagate redundant information or obscure complementary details. To address this issue, we propose Tri-view Consensus-Guided Fusion (TriCGF) for jointly modeling spectrogram, Mel-frequency cepstral coefficients, and HuBERT representations. TriCGF organizes each view into common and view-specific components before fusion. Cross-view Consensus Learning aggregates the common components into a global reference, while View-wise Gated Integration adaptively combines this reference with each view-specific component. A soft difference regularizer further discourages excessive information overlap. Under speaker-independent evaluation, TriCGF achieves 74.19% weighted accuracy (WA) and 75.17% unweighted accuracy (UA) on IEMOCAP, and 94.36% WA and 94.28% UA on EmoDB, outperforming representative SER methods on both datasets.
