This paper presents the application of morpheme-based and factored language models in an Amharic speech recognition task. Since the use of morphemes in both acoustic and language models often results in performance degradation due to a higher acoustic confusability and since it is problematic to use factored language models in standard word decoders, we applied the models in a lattice rescoring framework. Lattices of 100 best alternatives for each test sentence of the 5k development test set have been generated using a baseline speech recognizer with a word-based backoff bigram language model. The lattices have then been rescored by means of various morpheme-based and factored language models. A slight improvement in word recognition accuracy has been observed with morpheme-based language models while factored language models led to notable improvements in word recognition accuracy.