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As cars become indispensable parts of human daily life, a safe and comfortable driving environment is more desirable. The traditional touch-based interaction in cockpit is easy to distract the drivers' attention, leading to inefficient operations and potential security risks. Therefore, as a natural user interface (NUI), speech-based interaction has attracted more attention. In-car speech-based interaction endeavors to create a seamless driving and cabin experience for drivers and passengers through various speech processing applications, like speech recognition for command control, entertainment, navigation, and more. Differing from the commonly used automatic speech recognition (ASR) systems deployed in household or meeting scenarios, in-car systems force exclusively challenges. Nevertheless, the lack of publicly available real-world in-car speech data has been a major obstacle to the advancement of the field. Therefore, we launch the ICASSP2024 In-Car Multi-Channel Automatic Speech Recognition Challenge (ICMC-ASR), tailored to the domain of speech recognition in complex driving conditions.
In this challenge, we will release a over 1000 hours of real-world recorded, multi-channel, multi-speaker, in-car conversational Mandarin speech data, which includes far-field data collected by distributed microphones placed in the car as well as near-field data collected by each participants' headset microphone. Additionally, over 400 hours of far-field microphones recorded real in-car noise will be available for participants to explore the data simulation technology. The challenge consists of 2 tracks composed of automatic speech recognition (ASR) and automatic speech diarization and recognition (ASDR), aiming to promote in-car automatic speech recognition research and explore challenging research problems accordingly. After the challenge, we plan to open source the data which will further afford the research community with continuous input in this area.