Continuous speech segmentation using local adaptive thresholding technique in the blocking block area method

Abstract

Continuous speech is a form of natural human speech that is continuous without a clear boundary between words. In continuous speech recognition, a segmentation process is needed to cut the sentence at the boundary of each word. Segmentation becomes an important step because a speech can be recognized from the word segments produced by this process. The segmentation process in this study was carried out using local adaptive thresholding technique in the blocking block area method. This study aims to conduct performance comparisons for five local adaptive thresholding methods (Niblack, Sauvola, Bradley, Guanglei Xiong and Bernsen) in continuous speech segmentation to obtain the best method and optimum parameter values. Based on the results of the study, Niblack method is concluded as the best method for continuous speech segmentation in Indonesian language with the accuracy value of 95%, and the optimum parameter values for such method are window = 75 and k = 0.2.