DYNAMIC THRESHOLDING ON SPEECH SEGMENTATION

Abstract

Word is the preferred and natural unit of speech, b ecause word units have well defined acoustic repres entation. This paper presents several dynamic thresholding approaches for segment ing continuous Bangla speech sentences into words/s ub-words. We have proposed three efficient methods for speech segment ation: two of them are usually used in pattern clas sification (i.e., k-means and FCM clustering) and one of them is used in image se gmentation (i.e., Otsu’s thresholding method). We a lso used new approaches blocking black area and boundary detection techniqu es to properly detect word boundaries in continuous speech and label the entire speech sentence into a sequence of words/sub-words. K-Means and FCM clustering methods produce better segmentation results than that of Otsu’s Method. All the algorithms and metho ds used in this research are implemented in MATLAB and the proposed system achieved the average segmentation accuracy of 94% a