# AISHELL-NER: Named Entity Recognition from Chinese Speech

arXiv: https://arxiv.org/abs/2202.08533 · HTML: https://mlsanity.com/paper/2202.08533/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 22 references that are in the map.
- First posted on arXiv 17 Feb 2022.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline](https://mlsanity.com/paper/1709.05522/) (2017)
- [A Comparative Study on Transformer vs RNN in Speech Applications](https://mlsanity.com/paper/1909.06317/) (2019)
- [Named Entity Recognition with Bidirectional LSTM-CNNs](https://mlsanity.com/paper/1511.08308/) (2015)
- [An analysis of incorporating an external language model into a sequence-to-sequence model](https://mlsanity.com/paper/1712.01996/) (2017)
- [Chinese NER Using Lattice LSTM](https://mlsanity.com/paper/1805.02023/) (2018)
- [FLAT: Chinese NER Using Flat-Lattice Transformer](https://mlsanity.com/paper/2004.11795/) (2020)
- [End-to-end Named Entity Recognition from English Speech](https://mlsanity.com/paper/2005.11184/) (2020)
- [Audio De-identification: A New Entity Recognition Task](https://mlsanity.com/paper/1903.07037/) (2019)

## Built on by (most cited first, 19 of 19)

- [CopyNE: Better Contextual ASR by Copying Named Entities](https://mlsanity.com/paper/2305.12839/) (2023)
- [Using Large Language Model for End-to-End Chinese ASR and NER](https://mlsanity.com/paper/2401.11382/) (2024)
- [Joint Speech Translation and Named Entity Recognition](https://mlsanity.com/paper/2210.11987/) (2022)
- [DANCER: Entity Description Augmented Named Entity Corrector for Automatic Speech Recognition](https://mlsanity.com/paper/2403.17645/) (2024)
- [Large Language Model Should Understand Pinyin for Chinese ASR Error Correction](https://mlsanity.com/paper/2409.13262/) (2024)
- [Towards Human-Like Interactive Speech Recognition With Agentic Correction and Semantic Evaluation](https://mlsanity.com/paper/2605.29430/) (2026)
- [Double Mixture: Towards Continual Event Detection from Speech](https://mlsanity.com/paper/2404.13289/) (2024)
- [Medical Spoken Named Entity Recognition](https://mlsanity.com/paper/2406.13337/) (2024)
- [Improving Contextual ASR via Multi-grained Fusion with Large Language Models](https://mlsanity.com/paper/2507.12252/) (2025)
- [Enhancing the Robustness of Contextual ASR to Varying Biasing Information Volumes Through Purified Semantic Correlation Joint Modeling](https://mlsanity.com/paper/2509.05908/) (2025)
- [Mining Word Boundaries in Speech as Naturally Annotated Word Segmentation Data](https://mlsanity.com/paper/2210.17122/) (2022)
- [Leveraging Cross-Lingual Transfer Learning in Spoken Named Entity Recognition Systems](https://mlsanity.com/paper/2307.01310/) (2023)
- [CPPF: A contextual and post-processing-free model for automatic speech recognition](https://mlsanity.com/paper/2309.07413/) (2023)
- [Mining Word Boundaries from Speech-Text Parallel Data for Cross-domain Chinese Word Segmentation](https://mlsanity.com/paper/2412.09045/) (2024)
- [Joint Automatic Speech Recognition And Structure Learning For Better Speech Understanding](https://mlsanity.com/paper/2501.07329/) (2025)
- [UniSLU: Unified Spoken Language Understanding from Heterogeneous Cross-Task Datasets](https://mlsanity.com/paper/2507.12951/) (2025)
- [Generative Annotation for ASR Named Entity Correction](https://mlsanity.com/paper/2508.20700/) (2025)
- [PARCO: Phoneme-Augmented Robust Contextual ASR via Contrastive Entity Disambiguation](https://mlsanity.com/paper/2509.04357/) (2025)
- [JSPG: Dynamic Dictionary Filtering via Joint Semantic-Pinyin-Glyph Retrieval for Chinese Contextual ASR](https://mlsanity.com/paper/2605.16896/) (2026)

## Abstract (arXiv)

Named Entity Recognition (NER) from speech is among Spoken Language Understanding (SLU) tasks, aiming to extract semantic information from the speech signal. NER from speech is usually made through a two-step pipeline that consists of (1) processing the audio using an Automatic Speech Recognition (ASR) system and (2) applying an NER tagger to the ASR outputs. Recent works have shown the capability of the End-to-End (E2E) approach for NER from English and French speech, which is essentially entity-aware ASR. However, due to the many homophones and polyphones that exist in Chinese, NER from Chinese speech is effectively a more challenging task. In this paper, we introduce a new dataset AISEHLL-NER for NER from Chinese speech. Extensive experiments are conducted to explore the performance of several state-of-the-art methods. The results demonstrate that the performance could be improved by combining entity-aware ASR and pretrained NER tagger, which can be easily applied to the modern SLU pipeline. The dataset is publicly available at github.com/Alibaba-NLP/AISHELL-NER.