从文字到声音：广播故事声效检索初探
From Text to Sound: A Preliminary Study on Retrieving Sound Effects to Radio Stories
摘要：
音效在制作高质量的广播故事中起着至关重要的作用，但需要巨大的劳动力成本添加。在本文中，我们解决了自动添加音效的问题，以检索为基础的模型广播故事。然而，直接实现基于标签的检索模型会导致高误报，由于故事内容的歧义。为了解决这个问题，我们引入了一个基于检索的框架与语义推理模型，这有助于实现强大的检索结果杂交。我们的模型依赖于从候选触发器的上下文中提取的精心设计的特征。我们通过众包收集了两个故事配音数据集，分析了添加声音效果的设置，并对我们提出的方法进行了训练和测试。我们进一步讨论了每个功能的重要性，并介绍了几个启发式规则之间的权衡精度和召回。再加上文本到语音的技术，我们的研究结果揭示了一个有前途的自动流水线生产高质量的广播故事。
英文摘要：
Sound effects play an essential role in producing high-quality radio stories but require enormous labor cost to add. In this paper, we address the problem of automatically adding sound effects to radio stories with a retrieval-based model. However, directly implementing a tag-based retrieval model leads to high false positives due to the ambiguity of story contents. To solve this problem, we introduce a retrieval-based framework hybridized with a semantic inference model which helps to achieve robust retrieval results. Our model relies on fine-designed features extracted from the context of candidate triggers. We collect two story dubbing datasets through crowdsourcing to analyze the setting of adding sound effects and to train and test our proposed methods. We further discuss the importance of each feature and introduce several heuristic rules for the trade-off between precision and recall. Together with the text-to-speech technology, our results reveal a promising automatic pipeline on producing high-quality radio stories.
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。
