CLARA：面向音频表征获取的多语言对比学习
CLARA: Multilingual Contrastive Learning for Audio Representation Acquisition
AI总结：
CLARA通过多语言对比学习与自监督方法，在有限标注数据下实现跨语言情绪感知的语音表征，并在零样本、少样本及低资源语言场景中取得优异表现。
摘要：
多语言语音处理需要理解情绪，而这一任务因标注数据有限而变得困难。CLARA 最大限度地减少对标注数据的依赖，增强跨语言泛化能力。它擅长促进共享表征，即使在数据很少的情况下也有助于语音和情绪的跨语言迁移。我们的方法能够熟练捕捉语音中的情绪细微差别，克服主观评估问题。利用大规模多语言音频语料库和自监督学习，CLARA 开发出富含情绪信息的语音表征，推动情绪感知的多语言语音处理。我们的方法通过数据增强、用于视觉理解的文本嵌入来扩展数据范围，并将知识从高资源语言迁移到低资源语言。CLARA 在情绪识别、语言理解和音频基准测试中表现出色，在零样本和少样本学习中表现优异。它能够适应低资源语言，标志着多语言语音表征学习的进步。
英文摘要：
Multilingual speech processing requires understanding emotions, a task made difficult by limited labelled data. CLARA, minimizes reliance on labelled data, enhancing generalization across languages. It excels at fostering shared representations, aiding cross-lingual transfer of speech and emotions, even with little data. Our approach adeptly captures emotional nuances in speech, overcoming subjective assessment issues. Using a large multilingual audio corpus and self-supervised learning, CLARA develops speech representations enriched with emotions, advancing emotion-aware multilingual speech processing. Our method expands the data range using data augmentation, textual embedding for visual understanding, and transfers knowledge from high- to low-resource languages. CLARA demonstrates excellent performance in emotion recognition, language comprehension, and audio benchmarks, excelling in zero-shot and few-shot learning. It adapts to low-resource languages, marking progress in multilingual speech representation learning.
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。
