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 describes our submission to ICASSP 2023 MUG Challenge Track 4,
Keyphrase Extraction, which aims to extract keyphrases most relevant to the
conference theme from conference materials. We model the challenge as a
single-class Named Entity Recognition task and developed techniques for better
performance on the challenge: For the data preprocessing, we encode the split
keyphrases after word segmentation. In addition, we increase the amount of
input information that the model can accept at one time by fusing multiple
preprocessed sentences into one segment. We replace the loss function with the
multi-class focal loss to address the sparseness of keyphrases. Besides, we
score each appearance of keyphrases and add an extra output layer to fit the
score to rank keyphrases. Exhaustive evaluations are performed to find the best
combination of the word segmentation tool, the pre-trained embedding model, and
the corresponding hyperparameters. With these proposals, we scored 45.04 on the
final test set.

该研究介绍了他们提交的 ICASSP 2023 MUG 挑战赛第 4 轨道 —— 关键短语提取，并描述了他们开发的技术以改进关键短语提取的性能，包括采用命名实体识别任务的单类建模，用多类焦点损失函数替换丢失函数等。