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In this paper, large vocabulary children's speech recognition is investigated by using the Deep Neural Network - Hidden Markov Model (DNN-HMM) hybrid and the Subspace Gaussian Mixture Model (SGMM) acoustic modeling approach. In the investigated scenario training data is limited to about 7 hours of speech from children in the age range 7-13 and testing data consists in read clean speech from children in the same age range. To tackle inter-speaker acoustic variability, speaker adaptive training, based on feature space maximum likelihood linear regression, as well as vocal tract length normalization are adopted. Experimental results show that with both DNN-HMM and SGMM systems very good recognition results can be achieved although best results are obtained with the DNN-HMM system.