Efficient Audio-visual Cross-modal Speaker Tagging via Supervised Joint Correspondence Auto-encoder

LIU Xin; LI Heyang; ZHONG Bineng; DU Jixiang

doi:10.11999/JEIT171011

Volume 40 Issue 7

Jul. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(7): 1635-1642

MA Jitong, QIU Tianshuang, LI Rong, XIA Nan, LI Jingchun. Concurrent Blind Equalization Algorithm Based on Probability Density Function Matching and Fractional Lower Order Moments[J]. Journal of Electronics & Information Technology, 2017, 39(7): 1532-1538. doi: 10.11999/JEIT160841

Citation:

LIU Xin, LI Heyang, ZHONG Bineng, DU Jixiang. Efficient Audio-visual Cross-modal Speaker Tagging via Supervised Joint Correspondence Auto-encoder[J]. Journal of Electronics & Information Technology, 2018, 40(7): 1635-1642. doi: 10.11999/JEIT171011

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Efficient Audio-visual Cross-modal Speaker Tagging via Supervised Joint Correspondence Auto-encoder

doi: 10.11999/JEIT171011

LIU Xin^①② LI Heyang^① ZHONG Bineng^①② DU Jixiang^①②

Funds:

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)

Received Date: 2017-10-30
Rev Recd Date: 2018-04-10
Publish Date: 2018-07-19

Abstract

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.
- Cross-modal speaker tagging、Supervised joint correspondence auto-encoder、Softmax regression、Supervised neural network model,

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References(21)

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