Automatic Speech Recognition (ASR) systems can perform better if trained for a specific application. Though, since we require a huge amount of information to train models it is not feasible to build such systems once ready for the user, but we could use adaptation to make the ASR system more appropriate for the final use. In this work we address adaptation for the vocal characteristics of the speaker, environmental noise and the language model. Acoustic model adaptation is done by Speaker Adaptive Training (SAT), linear Vocal Tract Length Normalization (lVTLN) and constrained Maximum Likelihood Linear Regression (cMLLR). Interpolation is applied for language model adaptation. The relative WER reduction by using cMLLR was9.44%. The perplexity of the language model could be relatively improved by 14.47%