Speech recognition based on the transformer's multi-head attention in Arabic

The Transformer model is frequently employed for speech command recognition (SCR) since it supports parallelization and has internal attention. The high learning speed of this design and the absence of sequential operation, like with recurrent neural networks, are its two greatest advantages. In this work, Transformer models and data augmentation techniques to enhance the used dataset were considered to build a system for the automatic command recognition of Arabic speech. Little information is available for developing voice recognition systems in Arabic, which is recognized as a component of numerous significant languages. This study examines how the Transformer network is affected by hyperparameters to address two questions: Which hyperparameter is crucial for both task effectiveness and training efficiency? According to the results of our tests, it was found that using a Transformer with hyperparameter optimization helped the Arabic SCR system operate better.