Kurdish Speech to Text Recognition System Based on Deep Convolutional-recurrent Neural Networks

Abstract

In recent years, deep learning has had enormous success in speech recognition and natural language processing. In other languages, recent progress in speech recognition has been quite promising, but the Kurdish language has not seen comparable development. There are extremely few research papers on Kurdish speech recognition. In this paper, investigated Gated Recurrent Units (GRUs) which is one of the popular RNN models to recognize individual Kurdish words, and propose a very simplified deep-learning architecture to get more efficient and high accuracy model. The proposed model consists of a combination of CNN and GRU layers. The Kurdish Sorani Speech KSS dataset was created for the speech recognition system, as its 18799 sound files for 500 formal Kurdish words. Finally, the model proposed was trained with collected data and yielded over %96 accuracy. The combination of CNN an RNN (GURs) for speech recognition achieved superior performance compared to the other feed-forward deep neural network models and other statistical methods.