In this paper, we make use of hidden Markov model (HMM) state alignment information in i-vector/probabilistic linear discriminant analysis (PLDA) framework to improve the verification performance in a text-dependent single utterance (TDSU) task. In the TDSU task, speakers repeat a fixed utterance in both enrollment and authentication sessions. Despite Gaussian mixture models (GMMs) have been the dominant modeling technique for text-independent applications, an HMM based method might be better suited for the TDSU task since it captures the co-articulation information better. Recently, powerful channel compensation techniques such as joint factor analysis (JFA), i-vectors and PLDA have been proposed for GMM based text-independent speaker verification. In this study, we train a separate i-vector/PLDA model for each sentence HMM state in order to utilize the alignment information of the HMM states in a TDSU task. The proposed method is tested using a multi-channel speaker verification database. In the experiments, it is observed that HMM state based i-vector/PLDA (i-vector/PLDA-HMM) provides approximately 67% relative reduction in equal error rate (EER) when compared to the i-vector/PLDA. The proposed method also outperforms the baseline GMM and sentence HMM methods. It yields approximately 51% relative reduction in EER over the best performing sentence HMM method.