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Efficient Audio-visual Cross-modal Speaker Tagging via Supervised Joint Correspondence Auto-encoder |
LIU Xin①② LI Heyang① ZHONG Bineng①② DU Jixiang①② |
①(Institute of Computer Science and Technology, Huaqiao University, Xiamen 361021, China)
②(Xiamen Key Laboratory of Computer Vision and Pattern Recognition, Xiamen 361021, China) |
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Abstract Cross-modal speaker tagging aims to learn the latent relationship between different biometrics for mutual annotation, which can potentially be utilized in various human-computer interactions. In order to solve the “semantic gap” between the face and audio modalities, this paper presents an efficient supervised joint correspondence auto-encoder to link the face and audio counterpart, where by the speaker can be crosswise tagged. First, Convolutional Neural Network (CNN) and Deep Belief Network (DBN) are used to extract the discriminative features of the face and the audio samples respectively. Then, a supervised neural network model associated with softmax regression is embedded into a joint auto-encoder model, which can discriminatively preserving the inter-modal and intra-modal similarities. Accordingly, three different kinds of supervised joint correspondence auto-encoder models are presented to correlate the semantic relationships between the face and the audio counterparts, and the speaker can be crosswise annotated efficiently. The experimental results show that the proposed supervised joint auto-encoder is able to perform cross-modal speaker tagging with outstanding performance, and demonstrate the robustness to facial posture variations and sample diversities.
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Received: 30 October 2017
Published: 11 May 2018
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Fund:The National Natural Science Foundation of China (61673185, 61572205, 61673186), The Natural Science Foundation of Fujian Province (2017J01112), The Promotion Program for Young and Middle-aged Teacher in Science and Technology Research of Huaqiao University (ZQN-309) |
Corresponding Authors:
LIU Xin
E-mail: xliu@hqu.edu.cn
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