Speech recognition of Arabic words using artificial neural networks

الملخص EN

The speech recognition system has been widely used by many researchers using different methods to fulfill a fast and accurate system.

Speech signal recognition is a typical classification problem, which generally includes two main parts : feature extraction and classification.

In this paper, a new approach to achieve speech recognition task is proposed by using transformation techniques for feature extraction methods ; namely, slantlet transform (SLT), discrete wavelet transforms (DWT) type Daubechies Db1 and Db4.

Furthermore, a modified artificial neural network (ANN) with dynamic time warping (DTW) algorithm is developed to train a speech recognition system to be used for classification and recognition purposes.

Twenty three Arabic words were recorded fifteen different times in a studio by one speaker to form a database.

The performance of the proposed system using this database has been evaluated by computer simulation using MATLAB package.

The result shows recognition accuracy of 65 %, 70 % and 80 % using DWT (Db1), DWT (Db4) and SLT respectively.