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

Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022), pages 6786–6793 Marseille, 20-25 June 2022 © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0 6786 CI-AVSR: A Cantonese Audio-Visual Speech Dataset for In-car Command Recognition Wenliang Dai★, Samuel Cahyawijaya★, Tiezheng Yu★, Elham J. Barezi★, Peng Xu†, Cheuk Tung Shadow Yiu, Rita Frieske, Holy Lovenia, Genta Indra Winata†, Qifeng Chen, Xiaojuan Ma, Bertram E. Shi, Pascale Fung The Hong Kong University of Science and Technology {wdaiai,scahyawijaya,tyuah,eebarezi}@connect.ust.hk Abstract With the rise of deep learning and intelligent vehicles, the smart assistant has become an essential in-car component to facilitate driving and provide extra functionalities. In-car smart assistants should be able to process general as well as car-related commands and perform corresponding actions, which eases driving and improves safety. However, there is a data scarcity issue for low resource languages, hindering the development of research and applications. In this paper, we introduce a new dataset, Cantonese In-car Audio-Visual Speech Recognition (CI-AVSR), for in-car command recognition in the Cantonese language with both video and audio data. It consists of 4,984 samples (8.3 hours) of 200 in-car commands recorded by 30 native Cantonese speakers. Furthermore, we augment our dataset using common in-car background noises to simulate real environments, producing a dataset 10 times larger than the collected one. We provide detailed statistics of both the clean and the augmented versions of our dataset. Moreover, we implement two multimodal baselines to demonstrate the validity of CI-AVSR. Experiment results show that leveraging the visual signal improves the overall performance of the model. Although our best model can achieve a considerable quality on the clean test set, the speech recognition quality on the noisy data is still inferior and remains an extremely challenging task for real in-car speech recognition systems. The dataset and code will be released at https://github.com/HLTCHKUST/CI-AVSR. Keywords: Multimodal, Audio-visual, Automatic Speech Recognition, Cantonese, Smart Assistant 1. Introduction The research of intelligent transportation systems covers a broad spectrum, including monitoring the driver’s inattention (Haghani et al., 2021), traffic monitoring for both surveillance and traffic management (Buch et al., 2011), and video-based lane tracking for smart driving assistance (Tang et al., 2021). More recently, the area of self-driving (or autonomous driving) has been developed rapidly with a lot of attention from both academia (Van Brummelen et al., 2018; Zhou et al., 2019) and the industry. Autonomous driving aims to notably improve driving convenience and safety by automating some of the driv- ing tasks, helping drivers to focus more on driving itself. The fast prosperous growth of artificial intelligence (AI) and deep neural networks has led to the growth of au- tonomous driving. However, there is still a long journey to meet the highest level of automation required for autonomous driving systems to deal with various road environments and noise conditions for various driver languages and abilities (Zhou et al., 2019). Voice controllable systems (VCS), which lean on Auto- matic Speech Recognition (ASR) methods, have gained remarkable progress with the advances in deep learn- ing and …

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