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arXiv · 2505.23036

AISHELL-5: The First Open-Source In-Car Multi-Channel Multi-Speaker Speech Dataset for Automatic Speech Diarization and Recognition

Yuhang Dai·He Wang·Xingchen Li·Zihan Zhang·Shuiyuan Wang·Lei Xie·Xin Xu·Hongxiao Guo·Shaoji Zhang·Hui Bu·Wei Chen
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Abstract

This paper delineates AISHELL-5, the first open-source in-car multi-channel multi-speaker Mandarin automatic speech recognition (ASR) dataset. AISHLL-5 includes two parts: (1) over 100 hours of multi-channel speech data recorded in an electric vehicle across more than 60 real driving scenarios. This audio data consists of four far-field speech signals captured by microphones located on each car door, as well as near-field signals obtained from high-fidelity headset microphones worn by each speaker. (2) a collection of 40 hours of real-world environmental noise recordings, which supports the in-car speech data simulation. Moreover, we also provide an open-access, reproducible baseline system based on this dataset. This system features a speech frontend model that employs speech source separation to extract each speaker's clean speech from the far-field signals, along with a speech recognition module that accurately transcribes the content of each individual speaker. Experimental results demonstrate the challenges faced by various mainstream ASR models when evaluated on the AISHELL-5. We firmly believe the AISHELL-5 dataset will significantly advance the research on ASR systems under complex driving scenarios by establishing the first publicly available in-car ASR benchmark.

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Yuhang Dai, He Wang, Xingchen Li, Zihan Zhang, Shuiyuan Wang, Lei Xie, Xin Xu, Hongxiao Guo, Shaoji Zhang, Hui Bu, Wei Chen. 2025-05-29. AISHELL-5: The First Open-Source In-Car Multi-Channel Multi-Speaker Speech Dataset for Automatic Speech Diarization and Recognition. https://arxiv.org/abs/2505.23036

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