基础模型辅助的自动语音情感识别：转录、标注与增强
Foundation Model Assisted Automatic Speech Emotion Recognition: Transcribing, Annotating, and Augmenting
AI总结：
本研究探索利用基础模型自动化语音情感识别的转录、标注与增强过程，发现其能提升系统性能并验证了标注无标签语音样本扩充数据集的可行性。
摘要：
使用深度学习模型的语音情感识别（SER）正在取得重大进展。尽管如此，训练SER系统仍然具有挑战性，需要耗费时间和昂贵的资源。与许多其他机器学习任务一样，获取SER数据集需要大量的数据标注工作，包括转录和标记。这些标注过程在尝试扩展传统SER系统时带来了挑战。基础模型的最新发展产生了巨大影响，催生了ChatGPT等应用。这些模型增强了人机交互，包括为SER等领域简化数据收集带来了独特的可能性。在本研究中，我们探索使用基础模型来辅助自动化SER，从转录、标注到增强。我们的研究表明，这些模型可以生成转录文本，以增强仅依赖语音数据的SER系统的性能。此外，我们注意到从转录语音中标注情感仍然是一项具有挑战性的任务。然而，结合多个LLM的输出可以提高标注质量。最后，我们的发现表明，通过标注无标签语音样本来增强现有语音情感数据集是可行的。
英文摘要：
Significant advances are being made in speech emotion recognition (SER) using deep learning models. Nonetheless, training SER systems remains challenging, requiring both time and costly resources. Like many other machine learning tasks, acquiring datasets for SER requires substantial data annotation efforts, including transcription and labeling. These annotation processes present challenges when attempting to scale up conventional SER systems. Recent developments in foundational models have had a tremendous impact, giving rise to applications such as ChatGPT. These models have enhanced human-computer interactions including bringing unique possibilities for streamlining data collection in fields like SER. In this research, we explore the use of foundational models to assist in automating SER from transcription and annotation to augmentation. Our study demonstrates that these models can generate transcriptions to enhance the performance of SER systems that rely solely on speech data. Furthermore, we note that annotating emotions from transcribed speech remains a challenging task. However, combining outputs from multiple LLMs enhances the quality of annotations. Lastly, our findings suggest the feasibility of augmenting existing speech emotion datasets by annotating unlabeled speech samples.
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。
