Unvoiced and Voiced Speech Segmentation Based on the Dimension of Signal Local Linear Manifold

Abstract

A new method of unvoiced and voiced speech segmentation is proposed from the perspective of local linear manifold analysis of speech signal. It is based on the estimation of the dimension of short-time linear subspace. The subspace dimensional characteristics of the single phoneme signal is studied. The local signal vector set is analyzed by using PCA algorithm to estimate the dimension of the data matrix formed by framing. The local PCA is used to analyze the speech signal to achieve the segmentation of unvoiced and voiced pronunciation. Simulation experiments prove the effectiveness of the proposed method.