Spoken Word Recognition Using Slantlet Transform and Dynamic Time Warping

Spoken Word Recognition Using Slantlet Transform and Dynamic Time Warping

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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 Clear Glass Can generally includes two main parts: feature extraction and classification.In this work, three feature extraction methods, namely SLT, DWT Db1 and DWT Db4, were compared.The dynamic time warping (DTW) algorithm is used for recognition.

Twenty three Arabic words were recorded fifteen different times in a studio by one speaker to form a database.The proposed system was evaluated using this database.The result shows recognition accuracy of 93.04%, 92.

17% BOSCH Serie 8 HBG6764S6B Electric Smart Oven - Stainless Steel and 94.78% using DWT Db1, DWT Db4 and SLT respectively.