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In recent years, significant progress has been made in automatic lip reading.But these methods require large-scale datasets that do not exist for manylow-resource languages. In this paper, we have presented a new multipurposeaudio-visual dataset for Persian. This dataset consists of almost 220 hours ofvideos with 1760 corresponding speakers. In addition to lip reading, thedataset is suitable for automatic speech recognition, audio-visual speechrecognition, and speaker recognition. Also, it is the first large-scale lipreading dataset in Persian. A baseline method was provided for each mentionedtask. In addition, we have proposed a technique to detect visemes (a visualequivalent of a phoneme) in Persian. The visemes obtained by this methodincrease the accuracy of the lip reading task by 7% relatively compared to thepreviously proposed visemes, which can be applied to other languages as well.

A Multi-Purpose Audio-Visual Corpus for Multi-Modal Persian Speech Recognition: the Arman-AV Dataset