  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.
