To promote speech processing and recognition research in driving scenarios,
we build on the success of the Intelligent Cockpit Speech Recognition Challenge
(ICSRC) held at ISCSLP 2022 and launch the ICASSP 2024 In-Car Multi-Channel
Automatic Speech Recognition (ICMC-ASR) Challenge. This challenge collects over
100 hours of multi-channel speech data recorded inside a new energy vehicle and
40 hours of noise for data augmentation. Two tracks, including automatic speech
recognition (ASR) and automatic speech diarization and recognition (ASDR) are
set up, using character error rate (CER) and concatenated minimum permutation
character error rate (cpCER) as evaluation metrics, respectively. Overall, the
ICMC-ASR Challenge attracts 98 participating teams and receives 53 valid
results in both tracks. In the end, first-place team USTCiflytek achieves a CER
of 13.16% in the ASR track and a cpCER of 21.48% in the ASDR track, showing an
absolute improvement of 13.08% and 51.4% compared to our challenge baseline,
respectively.

促进驾驶场景下的语音处理和识别研究，建立在 ISCSLP 2022 举办的智能座舱语音识别挑战（ICSRC）成功的基础上，我们推出 ICASSP 2024 车载多通道自动语音识别（ICMC-ASR）挑战。该挑战收集了 100 多小时的多通道语音数据，记录在新能源汽车内，以及 40 小时的噪声用于数据增强。设立了两个赛道，包括自动语音识别（ASR）和自动语音日志记录与识别（ASDR），使用字符错误率（CER）和连接最小排列字符错误率（cpCER）作为评估指标。总体上，ICMC-ASR 挑战吸引了 98 支参赛团队，同时在两个赛道上收到了 53 份有效结果。最后，第一名的 USTCiflytek 团队在 ASR 赛道上取得 13.16% 的 CER，并在 ASDR 赛道上取得 21.48% 的 cpCER，与我们的挑战基线相比，在 ASR 方面绝对改进了 13.08%，在 ASDR 方面绝对改进了 51.4%。

ICMC-ASR: 2024 年 ICASSP 车载多通道自动语音识别挑战

ICMC-ASR: The ICASSP 2024 In-Car Multi-Channel Automatic Speech  Recognition Challenge

This paper summarizes our team's efforts in both tracks of the ICMC-ASR
Challenge for in-car multi-channel automatic speech recognition. Our submitted
systems for ICMC-ASR Challenge include the multi-channel front-end enhancement
and diarization, training data augmentation, speech recognition modeling with
multi-channel branches. Tested on the offical Eval1 and Eval2 set, our best
system achieves a relative 34.3% improvement in CER and 56.5% improvement in
cpCER, compared to the offical baseline system.

我们团队在 ICMC-ASR 挑战赛的两个方向上付出了努力，包括多通道前端增强和辨析、训练数据增强、多通道分支的语音识别模型，经测试，在官方 Eval1 和 Eval2 数据集上，我们的最佳系统相对于官方基准系统提高了 34.3% 的 CER 和 56.5% 的 cpCER。