用大模型生成未见实体的语音命名实体识别数据，降低成本。
"I've Heard of You!": Generate Spoken Named Entity Recognition Data for Unseen Entities
- 用大模型从实体词典生成句子，再通过语音合成生成语音数据。
- 在8,853个实体上实现零样本和跨域任务的SOTA性能。
- 适合需要低成本构建语音NER数据的研究者和开发者。
语音命名实体识别（Spoken NER）旨在从语音中识别命名实体，在语音处理中具有重要作用。然而，新实体每日涌现，标注其语音NER数据成本高昂。本文表明，现有语音NER系统对未见过的实体表现不佳。为此，我们提出一种基于命名实体词典（NED）生成语音NER数据的方法以降低标注成本。具体地，先使用大语言模型（LLM）从采样的实体生成句子，再通过文本转语音（TTS）系统生成语音；同时引入噪声度量过滤低质量数据。为评估方法，我们发布了一个包含8,853个实体的新基准及对应NED。实验结果表明，该方法在领域内、零样本域适应和完全零样本设置下均达到当前最优性能。数据将开源于https://github.com/DeepLearnXMU/HeardU。
原文摘要 · Abstract (English)
Spoken named entity recognition (NER) aims to identify named entities from speech, playing an important role in speech processing. New named entities appear every day, however, annotating their Spoken NER data is costly. In this paper, we demonstrate that existing Spoken NER systems perform poorly when dealing with previously unseen named entities. To tackle this challenge, we propose a method for generating Spoken NER data based on a named entity dictionary (NED) to reduce costs. Specifically, we first use a large language model (LLM) to generate sentences from the sampled named entities and then use a text-to-speech (TTS) system to generate the speech. Furthermore, we introduce a noise metric to filter out noisy data. To evaluate our approach, we release a novel Spoken NER benchmark along with a corresponding NED containing 8,853 entities. Experiment results show that our method achieves state-of-the-art (SOTA) performance in the in-domain, zero-shot domain adaptation, and fully zero-shot settings. Our data will be available at https://github.com/DeepLearnXMU/HeardU.
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