Real time Arabic speech recognition based on convolution neural network

Abstract

The development of voice-activated devices to perform handless operation makes modern systems like consumer electronics, audio systems, automobile electronics, household appliances and televisions are smarter. Real-time speech recognition is a major element in such applications, and research is ongoing to improve the functioning of these systems in Arabic languages as well. This paper proposes an Arabic speech recognition system dependent on the Convolution neural networks. The speech recognition model using Mel-Frequency Cepstrum Coefficients (MFCCs) for feature extraction after being processed to reduce noise and remove silence from speech signals. The performance of the proposed system is evaluated on dataset content 12000 samples Arabic spoken digits and commands from various dialects. The proposed system is a better accuracy of recognition 100% during training phases, accuracy of validity 93, and 91.625 % accuracy of the CNN model testing.

Subject Classification:

• Primary 93A30
• Secondary 49K15

Keywords:

• Convolution Neural Networks (CNN)
• Voice controlled devices
• Speech recognition