In this paper, a novel subspace projection approach is proposed for analysis of speech signal under stressed condition. The subspace projection method is based on the assumption of orthogonality between speech subspace and stress subspace. Speech and stress subspaces contain speech and stress information, respectively. The projection of stressed speech vectors onto the speech subspace will separate speech-specific information. In this work, the speech subspace consists of neutral speech vectors. Speech and stress recognition techniques are used to verify the orthogonal relation between speech and stress subspaces. The evaluation database consists of 119 word vocabulary under neutral, angry, sad and Lombard conditions. Hidden Markov models for speech and stress recognition are used with mel-frequency cepstral coefficient features for evaluation of estimated speech and stress information. © 2016, Springer Science+Business Media New York.