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This paper presents a sensory fusion neuromorphic dataset collected withprecise temporal synchronization using a set of Address-Event-Representationsensors and tools. The target application is the lip reading of severalkeywords for different machine learning applications, such as digits, roboticcommands, and auxiliary rich phonetic short words. The dataset is enlarged witha spiking version of an audio-visual lip reading dataset collected withframe-based cameras. LIPSFUS is publicly available and it has been validatedwith a deep learning architecture for audio and visual classification. It isintended for sensory fusion architectures based on both artificial and spikingneural network algorithms.

LIPSFUS: A neuromorphic dataset for audio-visual sensory fusion of lip reading