Audio-Visual Wake Word Spotting in MISP2021 Challenge
Dataset Release and Deep Analysis
Hengshun Zhou (University of Science and Technology of China)
Jun Du (University of Science and Technology of China)
Gongzhen Zou (University of Science and Technology of China)
Zhaoxu Nian (University of Science and Technology of China)
Chin Hui Lee (Georgia Institute of Technology)
Sabato Marco Siniscalchi (Georgia Institute of Technology, University of Enna Kore)
Shinji Watanabe (Carnegie Mellon University)
Odette Scharenborg (TU Delft - Multimedia Computing)
Jingdong Chen (Northwestern Polytechnical University)
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Abstract
In this paper, we describe and release publicly the audio-visual wake word spotting (WWS) database in the MISP2021 Challenge, which covers a range of scenarios of audio and video data collected by near-, mid-, and far-field microphone arrays, and cameras, to create a shared and publicly available database for WWS. The database and the code 2 are released, which will be a valuable addition to the community for promoting WWS research using multi-modality information in realistic and complex conditions. Moreover, we investigated the different data augmentation methods for single modalities on an end-to-end WWS network. A set of audio-visual fusion experiments and analysis were conducted to observe the assistance from visual information to acoustic information based on different audio and video field configurations. The results showed that the fusion system generally improves over the single-modality (audio- or video-only) system, especially under complex noisy conditions.