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In the task of skeleton-based action recognition, long-term temporal dependencies are significant cues for sequential skeleton data. State-of-the-art methods rarely have access to long-term temporal information, due to the limitations of their receptive fields. Meanwhile, most of the recent multiple branches methods only consider different input modalities but ignore the information in various temporal scales. To address the above issues, we propose a multi-scale temporal transformer (MTT) in this letter, for skeleton-based action recognition. Firstly, the raw skeleton data are embedded by graph convolutional network (GCN) blocks and multi-scale temporal embedding modules (MT-EMs), which are designed as multiple branches to extract features in various temporal scales. Secondly, we introduce transformer encoders (TE) to integrate embeddings and model the long-term temporal pattern. Moreover, we propose a …