LipFormer: Learning to Lipread Unseen Speakers Based on Visual-Landmark Transformers

Author:

Xue Feng1ORCID,Li Yu2ORCID,Liu Deyin3,Xie Yincen2ORCID,Wu Lin2ORCID,Hong Richang2ORCID

Affiliation:

1. School of Software, Hefei University of Technology, Hefei, China

2. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China

3. School of Artificial Intelligence, Anhui University, Hefei, China

Funder

National Natural Science Foundation of China

University Synergy Innovation Program of Anhui Province

Anhui Provincial Major Science and Technology Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Media Technology

Reference45 articles.

1. CTNet: Context-based Tandem Network for Semantic Segmentation

2. LCSNet: End-to-end Lipreading with Channel-aware Feature Selection

3. Efficient End-to-End Sentence-Level Lipreading with Temporal Convolutional Networks

4. Automatic lipreading with principle component analysis;yavuz;Proc 2nd Int Conf,2022

5. LipNet: End-to-end sentence-level lipreading;assael;arXiv 1611 01599,2016

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1. Generating dynamic lip-syncing using target audio in a multimedia environment;Natural Language Processing Journal;2024-09

2. HNet: A deep learning based hybrid network for speaker dependent visual speech recognition;International Journal of Hybrid Intelligent Systems;2024-06-03

3. LipNet: End-to-End Lipreading;Indian Journal of Data Mining;2024-05-30

4. Speech recognition in digital videos without audio using convolutional neural networks;Journal of Intelligent & Fuzzy Systems;2024-03-23

5. Mute Mate: An Advanced Lip-Reading Solution for Mute Participants in Virtual Conferences;Lecture Notes in Networks and Systems;2024

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