ACES：在声音语义上评估自动音频字幕模型
ACES: Evaluating Automated Audio Captioning Models on the Semantics of Sounds
AI总结：
本文提出ACES评估指标，从人类听觉语义解析角度结合语义相似度与实体标注评估自动音频字幕，并在Clotho-Eval FENSE基准上优于同类指标。
摘要：
自动音频字幕是一项多模态任务，旨在将音频内容转换为自然语言。音频字幕系统的评估通常基于应用于文本数据的定量指标。以往研究采用了源自机器翻译和图像字幕的指标来评估生成的音频字幕的质量。受听觉认知神经科学研究的启发，我们引入了一种新的指标方法——基于声音语义的音频字幕评估（ACES）。ACES考虑了人类听者如何从声音中解析语义信息，为自动音频字幕系统提供了一个新颖且全面的评估视角。ACES结合了语义相似度和语义实体标注。在Clotho-Eval FENSE基准的两个评估类别中，ACES优于类似的自动音频字幕指标。
英文摘要：
Automated Audio Captioning is a multimodal task that aims to convert audio content into natural language. The assessment of audio captioning systems is typically based on quantitative metrics applied to text data. Previous studies have employed metrics derived from machine translation and image captioning to evaluate the quality of generated audio captions. Drawing inspiration from auditory cognitive neuroscience research, we introduce a novel metric approach -- Audio Captioning Evaluation on Semantics of Sound (ACES). ACES takes into account how human listeners parse semantic information from sounds, providing a novel and comprehensive evaluation perspective for automated audio captioning systems. ACES combines semantic similarities and semantic entity labeling. ACES outperforms similar automated audio captioning metrics on the Clotho-Eval FENSE benchmark in two evaluation categories.
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。
