A precise speech recognition system usually is based on a large amount of training data with handcrafted transcription， which sets a barrier to the recognition of many low-resource languages. Acoustic model sharing， which is based on the similarity of certain rich and low resource language pair， provides a new method to solve the problem and helps to build an automatic speech recognition （ASR） system without any training data of the given low resource language. This paper expands the method to Korean speech recognition. Specifically， we train an acoustic model on Mandarin data， and lay down a set of mapping rules between Mandarin and Korean phonemes. A character error rate （CER） of 27.33% is achieved on Zeroth Korean test set without using any Korean speech data. Moreover， we also test the difference between source-to-target and target-to-source phoneme mapping rules， and prove that the latter is more appropriate for acoustic model sharing.