基于BAVED数据集的Wav2vec2 .0和HuBERT阿拉伯语语音情感识别
Arabic Speech Emotion Recognition Employing Wav2vec2.0 and HuBERT Based on BAVED Dataset
摘要：
近年来，在语音识别和自然语言处理领域取得了巨大的研究成果。这是由于wav2vec2.0、Wav2vecU、WavBERT和HuBERT等成熟的多层深度学习范式提供了更好的表示学习和高信息捕获能力。这样的范例运行在数百个未标记的数据上，然后针对特定任务在一个小数据集上进行微调。本文介绍了一种基于深度学习的阿拉伯语语音对话情感识别模型。开发的模型采用了最先进的音频表示，包括wav2vec2.0和HuBERT。我们的模型的实验和性能结果克服了以前已知的结果。
英文摘要：
Recently, there have been tremendous research outcomes in the fields of speech recognition and natural language processing. This is due to the well-developed multi-layers deep learning paradigms such as wav2vec2.0, Wav2vecU, WavBERT, and HuBERT that provide better representation learning and high information capturing. Such paradigms run on hundreds of unlabeled data, then fine-tuned on a small dataset for specific tasks. This paper introduces a deep learning constructed emotional recognition model for Arabic speech dialogues. The developed model employs the state of the art audio representations include wav2vec2.0 and HuBERT. The experiment and performance results of our model overcome the previous known outcomes.
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。
