A speech sectioning tool for corpus development

This paper aims to propose a technique of speech segmentation using zero-crossing (ZC) with an average of absolute amplitude to classify speech and non-speech by analyzing silence (sil) position and end point detection. To automatic segment speech file, we determine the silence position and speech position to specify point of sectioning. Thereafter, we compare the length of each sectioning file with approximation phones length of each file. With this method, we can solve the problem of uncertainty length of silence (sil) and short pause (sp) in one sentence, for example, silence shorter than short pause and short pause longer than silence. The proposed method evaluates with 2 types of data; 1) data with certainty length of silence and short pause and 2) data with uncertainty length of silence and short pause. The results of the experiments show that the accuracy rate of automatic speech sectioning is acceptable