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With the rise of deep learning and intelligent vehicle, the smart assistanthas become an essential in-car component to facilitate driving and provideextra functionalities. In-car smart assistants should be able to processgeneral as well as car-related commands and perform corresponding actions,which eases driving and improves safety. However, there is a data scarcityissue for low resource languages, hindering the development of research andapplications. In this paper, we introduce a new dataset, Cantonese In-carAudio-Visual Speech Recognition (CI-AVSR), for in-car command recognition inthe Cantonese language with both video and audio data. It consists of 4,984samples (8.3 hours) of 200 in-car commands recorded by 30 native Cantonesespeakers. Furthermore, we augment our dataset using common in-car backgroundnoises to simulate real environments, producing a dataset 10 times larger thanthe collected one. We provide detailed statistics of both the clean and theaugmented versions of our dataset. Moreover, we implement two multimodalbaselines to demonstrate the validity of CI-AVSR. Experiment results show thatleveraging the visual signal improves the overall performance of the model.Although our best model can achieve a considerable quality on the clean testset, the speech recognition quality on the noisy data is still inferior andremains as an extremely challenging task for real in-car speech recognitionsystems. The dataset and code will be released athttps://github.com/HLTCHKUST/CI-AVSR.

CI-AVSR: A Cantonese Audio-Visual Speech Dataset for In-car Command Recognition