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This paper presents a novel approach for dialectal Arabic speech recognition using a hybrid CNN-LSTM model combined with attention-based encoder-decoder methods. The study addresses challenges in recognizing various Arabic dialects due to their unique characteristics and lack of standardized data, achieving a word error rate of 57.02% on two datasets. The proposed method incorporates data augmentation techniques to enhance training data and improve recognition accuracy in automatic speech recognition system

Author

akalbangkit

Language

EN