Voiced/unvoiced Decision Based on Recurrence Quantification Analysis
基于定量递归分析的清浊音判决

Voiced/unvoiced decision is an important component in speech signal processing. In this paper, different topological structures in Recurrence Plots (RPs) are described for the different physical models of speech production. By statistically analyzing the determinism and the normalized maximal length of diagonal structures acquired from Recurrence Quantification Analysis (RQA), a flexible and efficient decision framework is proposed. Comparing with some traditional methods, the proposed algorithm has lower wrong decision rate. The method provides a new way for feature extraction and speech recognition.