  Building Spoken Language Understanding (SLU) systems that do not rely on
language specific Automatic Speech Recognition (ASR) is an important yet less
explored problem in language processing. In this paper, we present a
comparative study aimed at employing a pre-trained acoustic model to perform
SLU in low resource scenarios. Specifically, we use three different embeddings
extracted using Allosaurus, a pre-trained universal phone decoder: (1) Phone
(2) Panphone, and (3) Allo embeddings. These embeddings are then used in
identifying the spoken intent. We perform experiments across three different
languages: English, Sinhala, and Tamil each with different data sizes to
simulate high, medium, and low resource scenarios. Our system improves on the
state-of-the-art (SOTA) intent classification accuracy by approximately 2.11%
for Sinhala and 7.00% for Tamil and achieves competitive results on English.
Furthermore, we present a quantitative analysis of how the performance scales
with the number of training examples used per intent.
