关键词: 学习子空间方法, 应力, 变异语音分类, CLAFIC算法

Abstract: Speech under G-Force produces when speaker is under different accelerations of gravity, the stressful speech and normal speech have different energy distributions in the frequency domain. The whole frequency band is divided into eight sub-bands and the proportions of sub-band energy are used as classification feature. In the paper, a subspace based method is proposed to classify normal speech and speech under G-Force. The method adopts Class-Featuring Information Compression (CLAFIC) algorithm to construct initial subspaces for Learning Subspace Method (LSM), training of each subspace is rotated in different ways by LSM and classification result is used to adjust classification parameters. The experiments show that the method has good classification performance for G-Force/Normal speech and obtains the average classification rate of 95.9%.

Key words: LSM, G-Force, Stressful speech classification, CLAFIC algorithm