| Challenge: | Existing approaches to transcribe contextual named entities (NEs) treat entities as tokens and generate them token-by-token, which may result in incomplete transcriptions of entities. |
| Approach: | They propose a mechanism that can copy entities from the NE dictionary and reduce errors during entity transcription. |
| Outcome: | The proposed mechanism can copy entities from the NE dictionary, reducing errors during entity transcription, ensuring the completeness of the entity. |
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| Challenge: | Identifying, replacing and inserting replacement named entities synthesized using voice cloning into original audio reduces the likelihood of deanonymization. |
| Approach: | They propose to use a named entity recognition system built directly on speech to replace a masked language model and synthesize the replacement named entities using voice cloning. |
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SLICER: Sliced Fine-Tuning for Low-Resource Cross-Lingual Transfer for Named Entity Recognition (2022.emnlp-main)
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| Challenge: | Large multilingual models fail to successfully transfer to low-resource languages for zero-shot cross-lingual transfer . sliced fine-tuning for named entity recognition (SLICER) forces stronger token contextualization in the Transformer. |
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| Challenge: | End-to-end ASR models struggle to recognize uncommon domain-specific words due to limited audio context. |
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| Challenge: | ASR systems are often unable to recognize speech due to generic datasets and open-vocabulary modeling. |
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| Challenge: | Existing contextual biasing techniques require additional parameterization . state-of-the-art ASR systems often fail to recognize named entities or critical rare words . |
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Piotr Szymański, Lukasz Augustyniak, Mikolaj Morzy, Adrian Szymczak, Krzysztof Surdyk, Piotr Żelasko
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| Challenge: | Named entity recognition is usually made through a pipeline process that consists of processing audio and applying a NER to the audio outputs. |
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E2E Spoken Entity Extraction for Virtual Agents (2023.emnlp-industry)
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| Challenge: | Extensive research has been done to recognize entities in spoken input. |
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Yuang Li, Yinglu Li, Min Zhang, Chang Su, Jiawei Yu, Mengyao Piao, Xiaosong Qiao, Miaomiao Ma, Yanqing Zhao, Hao Yang
| Challenge: | End-to-end automatic speech recognition systems struggle to recognize rare name entities such as personal names, organizations and terminologies that are not frequently encountered in the training data. |
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Generative Annotation for ASR Named Entity Correction (2025.emnlp-main)
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Yuanchang Luo, Daimeng Wei, Shaojun Li, Hengchao Shang, Jiaxin Guo, Zongyao Li, Zhanglin Wu, Xiaoyu Chen, Zhiqiang Rao, Jinlong Yang, Hao Yang
| Challenge: | Existing named entity correction models fail to transcribe domain-speciffcnamed entities when theforms of the wrongly-transcribed words and the ground-truth entity are signiffcantly different. |
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