In this paper, we develop Automatic Speech Recognition (ASR) systems for multi-genre speech recognition of low-resource languages where training data is predominantly conversational speech but test data can be in one of the following genres: news broadcast, topical broadcast and conversational speech. ASR for low-resource languages is often developed by adapting a pre-trained model to a target language. When training data is predominantly from one genre and limited, the system's performance for other genres suffer. To handle such out-of-domain scenarios, we employ multitask adaptation by using auxiliary conversational speech data from other languages in addition to the target-language data. We aim to (1) improve adaptation through implicit data augmentation by adding other languages as auxiliary tasks, and (2) prevent the acoustic model from overfitting to the dominant genre in the training set. Pre-trained parameters are obtained from a multilingual model trained with data from 18 languages using the Lattice-Free Maximum Mutual Information (LF-MMI) criterion. The adaptation is performed with the LF-MMI criterion. We present results on MATERIAL datasets for three languages: Kazakh and Farsi and Pashto.