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该算法提出一个新的基于音频和文本的跨模态预训练模型, CTAL。通过大量的音频和文本对的两个代理任务来学习音频和文本之间的模态内和模态间的联系:屏蔽的语言建模和屏蔽的跨模态声学建模。在对多个下游音频和文本任务进行微调后,CTAL 模型在不同任务上有明显的改进,包括情感分类、情绪分析和说话人验证。其中在 IEMOCAP(Emotion Classification) 数据集上WA 达到73.95%,在MOSEI(Sentiment Analysis)上 F1 达到 81.01%。

Text Python

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