基于嘴部发音动作锚定的跨语料库语音情感识别改进方法
Mouth Articulation-Based Anchoring for Improved Cross-Corpus Speech Emotion Recognition
AI总结：
针对跨语料库语音情感识别中声学特征不稳定、易受干扰的问题，本文提出以情感特定的嘴部发音动作作为锚定约束，并在CREMA-D和MSP-IMPROV基准上验证其能有效提升跨领域情感迁移识别性能。
摘要：
跨语料库语音情感识别（SER）在众多实际应用中发挥着重要作用。传统的跨语料库情感迁移方法通常集中于调整声学特征，以使其与不同语料库、领域或标签对齐。然而，由于说话人差异、领域偏移和录音条件等因素，声学特征本身具有易变性且容易出错。为应对这些挑战，本研究采用一种新颖的对比方法，将情感特定的发音动作作为分析的核心要素。通过将重点转向更稳定、更一致的发音动作，我们旨在增强SER任务中的情感迁移学习。我们的研究利用CREMA-D和MSP-IMPROV语料库作为基准，并揭示了这些发音动作在共通性和可靠性方面的重要见解。研究结果凸显了嘴部发音动作作为一种更优约束条件的潜力，可用于改善不同环境或领域下的情感识别。
英文摘要：
Cross-corpus speech emotion recognition (SER) plays a vital role in numerous practical applications. Traditional approaches to cross-corpus emotion transfer often concentrate on adapting acoustic features to align with different corpora, domains, or labels. However, acoustic features are inherently variable and error-prone due to factors like speaker differences, domain shifts, and recording conditions. To address these challenges, this study adopts a novel contrastive approach by focusing on emotion-specific articulatory gestures as the core elements for analysis. By shifting the emphasis on the more stable and consistent articulatory gestures, we aim to enhance emotion transfer learning in SER tasks. Our research leverages the CREMA-D and MSP-IMPROV corpora as benchmarks and it reveals valuable insights into the commonality and reliability of these articulatory gestures. The findings highlight mouth articulatory gesture potential as a better constraint for improving emotion recognition across different settings or domains.
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。
