  Intent Recognition and Slot Identification are crucial components in spoken
language understanding (SLU) systems. In this paper, we present a novel
approach towards both these tasks in the context of low resourced and unwritten
languages. We present an acoustic based SLU system that converts speech to its
phonetic transcription using a universal phone recognition system. We build a
word-free natural language understanding module that does intent recognition
and slot identification from these phonetic transcription. Our proposed SLU
system performs competitively for resource rich scenarios and significantly
outperforms existing approaches as the amount of available data reduces. We
observe more than 10% improvement for intent classification in Tamil and more
than 5% improvement for intent classification in Sinhala. We also present a
novel approach towards unsupervised slot identification using normalized
attention scores. This approach can be used for unsupervised slot labelling,
data augmentation and to generate data for a new slot in a one-shot way with
only one speech recording
