学习情感判别表示用于零样本跨语言语音情感识别
Learning Emotion-discriminative Representations for Zero-Shot Cross-lingual Speech Emotion Recognition
AI总结：
针对零样本跨语言语音情感识别中语言分布不匹配和目标语言缺乏情感标注的问题，提出一种结合监督对比学习和说话人对抗学习的情感判别表示学习方法，显著提升了识别性能。
摘要：
零样本跨语言语音情感识别（SER）由于语言间的分布不匹配以及目标语言缺乏情感标注而仍然具有挑战性。在这种情况下，仅使用源语言数据训练的模型在评估未见过的目标语言时，常常会出现泛化能力下降的问题。为了解决这一局限性，我们提出了一种情感判别表示学习方法，该方法集成了监督对比学习和说话人对抗学习。对比学习促进了跨语言情感对齐，而说话人对抗学习则抑制了与说话人相关的线索，以鼓励说话人不变的表示。在零样本跨语言SER设置下的实验结果表明，与传统训练策略相比，所提出的方法显著提高了SER性能。
英文摘要：
Zero-shot cross-lingual speech emotion recognition (SER) remains challenging due to distribution mismatches across languages and the lack of emotion annotations in target language. Under such conditions, models trained solely on source-language data frequently suffer from degraded generalization when evaluated on unseen target languages. To address this limitation, we propose an emotion-discriminative representation learning method that integrates supervised contrastive learning and speaker adversarial learning. The contrastive learning promotes cross-lingual emotion alignment, while speaker adversarial learning suppresses speaker-related cues to encourage speaker-invariant representations. Experimental results under a zero-shot cross-lingual SER setting demonstrate that the proposed method significantly improves SER performance over conventional training strategies.
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。
