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Lipreading refers to understanding and further translating the speech of aspeaker in the video into natural language. State-of-the-art lipreading methodsexcel in interpreting overlap speakers, i.e., speakers appear in both trainingand inference sets. However, generalizing these methods to unseen speakersincurs catastrophic performance degradation due to the limited number ofspeakers in training bank and the evident visual variations caused by theshape/color of lips for different speakers. Therefore, merely depending on thevisible changes of lips tends to cause model overfitting. To address thisproblem, we propose to use multi-modal features across visual and landmarks,which can describe the lip motion irrespective to the speaker identities. Then,we develop a sentence-level lipreading framework based on visual-landmarktransformers, namely LipFormer. Specifically, LipFormer consists of a lipmotion stream, a facial landmark stream, and a cross-modal fusion. Theembeddings from the two streams are produced by self-attention, which are fedto the cross-attention module to achieve the alignment between visuals andlandmarks. Finally, the resulting fused features can be decoded to output textsby a cascade seq2seq model. Experiments demonstrate that our method caneffectively enhance the model generalization to unseen speakers.

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