  Automatic speech recognition is a difficult problem in pattern recognition
because several sources of variability exist in the speech input like the
channel variations, the input might be clean or noisy, the speakers may have
different accent and variations in the gender, etc. As a result, domain
adaptation is important in speech recognition where we train the model for a
particular source domain and test it on a different target domain. In this
paper, we propose a technique to perform unsupervised gender-based domain
adaptation in speech recognition using phonetic features. The experiments are
performed on the TIMIT dataset and there is a considerable decrease in the
phoneme error rate using the proposed approach.
