Automatic syllable segmentation algorithm of Chinese speech based on MF-DFA

Abstract

Automatic speech segmentation algorithm plays an important role in speech recognition and spoken term detection. A method called automatic syllable segmentation of Chinese speech based on multi-fractal detrended fluctuation analysis (MF-DFA) is explored in this study. The algorithm attempts to improve the precision and robustness of Chinese syllable segmentation. Firstly, the multi-fractal characteristics of Chinese syllables based on MF-DFA are explored. Secondly, to solve the problem with the unclear boundary of adjacent finals, which leads to the unsatisfactory precision rate of Chinese syllable segmentation in existing algorithms, two-stage voiced decision algorithm is introduced. Finally, the generation of dividing point works by detecting the extreme points of the first-order differential curve for each voiced segment. The experimental results indicated that the multi fractal characteristics based on MF-DFA possess good distinction and robustness, and the proposed algorithm outperforms the earlier approaches in terms of the performance of Chinese syllable segmentation even in low SNR.