In this paper, we investigate the performance of deep neural network (DNN) and Subspace Gaussian mixture model (SGMM) in low-resource condition. Even though DNN outperforms SGMM and continuous density hidden Markov models (CDHMM) for high-resource data, it degrades in performance while modeling low-resource data. Our experimental results show that SGMM outperforms DNN for limited transcribed data. To resolve this problem in DNN, we propose to train DNN containing bottleneck layer in two stages: First stage involves extraction of bottleneck features. In second stage, the extracted bottleneck features from first stage are used to train DNN having bottleneck layer. All our experiments are performed using two Indian languages (Tamil & Hindi) in Mandi database. Our proposed method shows improved performance when compared to baseline SGMM and DNN models for limited training data. © 2015 IEEE.