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arXiv · 2204.08686

Audio-Visual Wake Word Spotting System For MISP Challenge 2021

Yanguang Xu·Jianwei Sun·Yang Han·Shuaijiang Zhao·Chaoyang Mei·Tingwei Guo·Shuran Zhou·Chuandong Xie·Wei Zou·Xiangang Li
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Abstract

This paper presents the details of our system designed for the Task 1 of Multimodal Information Based Speech Processing (MISP) Challenge 2021. The purpose of Task 1 is to leverage both audio and video information to improve the environmental robustness of far-field wake word spotting. In the proposed system, firstly, we take advantage of speech enhancement algorithms such as beamforming and weighted prediction error (WPE) to address the multi-microphone conversational audio. Secondly, several data augmentation techniques are applied to simulate a more realistic far-field scenario. For the video information, the provided region of interest (ROI) is used to obtain visual representation. Then the multi-layer CNN is proposed to learn audio and visual representations, and these representations are fed into our two-branch attention-based network which can be employed for fusion, such as transformer and conformed. The focal loss is used to fine-tune the model and improve the performance significantly. Finally, multiple trained models are integrated by casting vote to achieve our final 0.091 score.

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Yanguang Xu, Jianwei Sun, Yang Han, Shuaijiang Zhao, Chaoyang Mei, Tingwei Guo, Shuran Zhou, Chuandong Xie, Wei Zou, Xiangang Li. 2022-04-19. Audio-Visual Wake Word Spotting System For MISP Challenge 2021. https://arxiv.org/abs/2204.08686

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