@inproceedings{dai-etal-2022-ci,
title = "{CI}-{AVSR}: A {C}antonese Audio-Visual Speech Datasetfor In-car Command Recognition",
author = "Dai, Wenliang and
Cahyawijaya, Samuel and
Yu, Tiezheng and
Barezi, Elham J. and
Xu, Peng and
Yiu, Cheuk Tung and
Frieske, Rita and
Lovenia, Holy and
Winata, Genta and
Chen, Qifeng and
Ma, Xiaojuan and
Shi, Bertram and
Fung, Pascale",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.731",
pages = "6786--6793",
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 \url{https://github.com/HLTCHKUST/CI-AVSR}.",
}
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%0 Conference Proceedings
%T CI-AVSR: A Cantonese Audio-Visual Speech Datasetfor In-car Command Recognition
%A Dai, Wenliang
%A Cahyawijaya, Samuel
%A Yu, Tiezheng
%A Barezi, Elham J.
%A Xu, Peng
%A Yiu, Cheuk Tung
%A Frieske, Rita
%A Lovenia, Holy
%A Winata, Genta
%A Chen, Qifeng
%A Ma, Xiaojuan
%A Shi, Bertram
%A Fung, Pascale
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F dai-etal-2022-ci
%X 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.
%U https://aclanthology.org/2022.lrec-1.731
%P 6786-6793
Markdown (Informal)
[CI-AVSR: A Cantonese Audio-Visual Speech Datasetfor In-car Command Recognition](https://aclanthology.org/2022.lrec-1.731) (Dai et al., LREC 2022)
ACL
- Wenliang Dai, Samuel Cahyawijaya, Tiezheng Yu, Elham J. Barezi, Peng Xu, Cheuk Tung Yiu, Rita Frieske, Holy Lovenia, Genta Winata, Qifeng Chen, Xiaojuan Ma, Bertram Shi, and Pascale Fung. 2022. CI-AVSR: A Cantonese Audio-Visual Speech Datasetfor In-car Command Recognition. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 6786–6793, Marseille, France. European Language Resources Association.