In this paper, we have documented our efforts towards developing a robust children's automatic speech recognition (ASR) system in limited data scenario. At first, we have explored the effect of in-domain data augmentation so as to deal with limitations posed by data scarcity. This also helps in developing a competitive baseline ASR system. Next, we have studied the affect of modeling glottal activity parameters along with spectrum-based front-end acoustic features like the Mel-frequency cepstral coefficients (MFCC). Finally, the impact of feature normalization through feature-space maximum likelihood linear regression (fMLLR) is explored. As a consequence of applying fMLLR and then concatenating the normalized MFCC features with glottal activity parameters, a relative reduction in character error rate by 40% over the baseline is obtained.