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Building Spoken Language Understanding (SLU) systems that do not rely onlanguage specific Automatic Speech Recognition (ASR) is an important yet lessexplored problem in language processing. In this paper, we present acomparative study aimed at employing a pre-trained acoustic model to performSLU in low resource scenarios. Specifically, we use three different embeddingsextracted using Allosaurus, a pre-trained universal phone decoder: (1) Phone(2) Panphone, and (3) Allo embeddings. These embeddings are then used inidentifying the spoken intent. We perform experiments across three differentlanguages: English, Sinhala, and Tamil each with different data sizes tosimulate high, medium, and low resource scenarios. Our system improves on thestate-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 scaleswith the number of training examples used per intent.

Intent Classification Using Pre-trained Language Agnostic Embeddings For Low Resource Languages