Automatic Audio Captioning (AAC) refers to the task of translating an audio
sample into a natural language (NL) text that describes the audio events,
source of the events and their relationships. Unlike NL text generation tasks,
which rely on metrics like BLEU, ROUGE, METEOR based on lexical semantics for
evaluation, the AAC evaluation metric requires an ability to map NL text
(phrases) that correspond to similar sounds in addition lexical semantics.
Current metrics used for evaluation of AAC tasks lack an understanding of the
perceived properties of sound represented by text. In this paper, wepropose a
novel metric based on Text-to-Audio Grounding (TAG), which is, useful for
evaluating cross modal tasks like AAC. Experiments on publicly available AAC
data-set shows our evaluation metric to perform better compared to existing
metrics used in NL text and image captioning literature.

本研究提出了一种基于文本到音频匹配（TAG）的新型度量标准，用于评估自然语言文本生成与音频事件描述任务，实验证明该标准在 NL 文本和图像字幕文献中的表现较现有指标更好。