In the present research, we explore several methods for transforming phoneme models from a language with acoustic models that have been trained (source language) to another, untrained language (target language). One approach uses acoustic distance-measures to automatically define the mapping from source to target phonemes. This is achieved by training basic models for the target language using a limited amount of training data and calculating the distance between the source models and target models. Naturally this approach requires some data from the target language. Another approach, which also requires some data from the target language, is to use acoustic adaptation for augmenting the source language acoustic models to better match the acoustic properties of the data in the target language. Phoneme recognition results of these approaches are compared to a reference recognizer that is well-trained on the target language.