AISHELL-NER: Named Entity Recognition from Chinese Speech
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.
Code (1)
Tasks
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NERspeech-recognitionSpeech RecognitionSpoken Language UnderstandingSimilar Papers 제목 키워드 기반
PARCO: Phoneme-Augmented Robust Contextual ASR via Contrastive Entity Disambiguation
Automatic speech recognition (ASR) systems struggle with domain-specific named entities, especially homophones. Contextual ASR improves recognition but often fails to capture fine-grained phoneme variations due to limite…
Entity DisambiguationSpeech RecognitionDANCER: Entity Description Augmented Named Entity Corrector for Automatic Speech Recognition
End-to-end automatic speech recognition (E2E ASR) systems often suffer from mistranscription of domain-specific phrases, such as named entities, sometimes leading to catastrophic failures in downstream tasks. A family of…
Automatic Speech RecognitionLanguage Modellingspeech-recognitionSpeech RecognitionAISHELL6-whisper: A Chinese Mandarin Audio-visual Whisper Speech Dataset with Speech Recognition Baselines
Whisper speech recognition is crucial not only for ensuring privacy in sensitive communications but also for providing a critical communication bridge for patients under vocal restraint and enabling discrete interaction …
Audio-Visual Speech RecognitionA Multitask Training Approach to Enhance Whisper with Contextual Biasing and Open-Vocabulary Keyword Spotting
The recognition of rare named entities, such as personal names and terminologies, is challenging for automatic speech recognition (ASR) systems, especially when they are not frequently observed in the training data. In t…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)DecoderHallucination+3Joint Automatic Speech Recognition And Structure Learning For Better Speech Understanding
Spoken language understanding (SLU) is a structure prediction task in the field of speech. Recently, many works on SLU that treat it as a sequence-to-sequence task have achieved great success. However, This method is not…
Automatic Speech Recognitionintent-classificationIntent ClassificationNER+3