Recently there has been increased interest in Automatic Speech Recognition (ASR) and Key Word Spotting (KWS) systems for low resource languages. One of the driving forces for this research di-rection is the IARPA Babel project. This paper describes some of the research funded by this project at Cambridge University, as part of the Lorelei team co-ordinated by IBM. A range of topics are dis-cussed including: deep neural network based acoustic models; data augmentation; and zero acoustic model resource systems. Perfor-mance for all approaches is evaluated using the Limited (approx-imately 10 hours) and/or Full (approximately 80 hours) language packs distributed by IARPA. Both KWS and ASR performance fig-ures are given. Though absolute performance varies from language to language, and keyword list, the approaches described show con-sistent trends over the languages investigated to date. Using com-parable systems over the five Option Period 1 languages indicates a strong correlation between ASR performance and KWS perfor-mance.