We describe the Arabic broadcast transcription system elded by IBM in the GALE Phase 4 machine translation evaluation. Key advances over our Phase 3.5 system include improvements to contextdependent modeling in vowelized Arabic acoustic models; the use of neural-network features provided by the International Computer Science Institute; Model M language models; a neural network language model that uses syntactic and morphological features; and improvements to our system combination strategy. These advances were instrumental in achieving a word error rate of 8.9% on the