Abstract
This work presents a novel model to recognize spoken digits in the Arabic language. Due to the transformer-based models' tremendous success in natural language processing (NLP), several attempts have been made to extend transformer-based designs to other domains, such as vision and audio. However, our approach consists of extracting and inputting Mel-spectrogram features into our model of the proposed audio Mel-spectrogram vision transformer (AMSVT) for training. The signal processing community has been interested in these models due to the successful use of vision transformers (ViT) in several computer vision applications. This is because signals are frequently recorded as spectrograms (using the Mel-spectrogram, for example), which may be given directly as input to vision transformers. Our model outperformed a group of models in terms of accuracy and time, such as convolutional neural network (CNN)-based and recurrent neural network (RNN)-based.
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