Linear transformation on speech subspace for analysis of speech under stress condition

In this work, a novel approach of linear transformation on speech subspace is used to preserve the properties of speech signal under stress condition. It is assumed that, there is another subspace called as speech subspace which exist and contains the properties of speech signal under neutral and stress conditions. Therefore, speech component of stress speech is determined by linear transformation on speech subspace. The dimension of speech subspace is taken to be comparatively higher than original length of feature vector of training database to capture the variations in properties of speech signals more appropriately under stress condition. The linear transformation matrix is estimated using the information of HMM which is used to model the training database (neutral speech). The HMM information is used in terms of supervector. All the experiments in this work are done by parametrizing neutral and stress speech as nonlinear (TEO-CB-Auto-Env) feature. Experimentally it is observed that, a linear relationship exist between stress speech subspace and speech subspace. After linear transformation on speech subspace, speech recognizer outperforms by 7.57 % (62.14 % to 69.71%) under angry stress condition.