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arXiv · 2006.08870

End-to-End Code Switching Language Models for Automatic Speech Recognition

Ahan M. R.·Shreyas Sunil Kulkarni
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Abstract

In this paper, we particularly work on the code-switched text, one of the most common occurrences in the bilingual communities across the world. Due to the discrepancies in the extraction of code-switched text from an Automated Speech Recognition(ASR) module, and thereby extracting the monolingual text from the code-switched text, we propose an approach for extracting monolingual text using Deep Bi-directional Language Models(LM) such as BERT and other Machine Translation models, and also explore different ways of extracting code-switched text from the ASR model. We also explain the robustness of the model by comparing the results of Perplexity and other different metrics like WER, to the standard bi-lingual text output without any external information.

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BibTeXRIS

Ahan M. R., Shreyas Sunil Kulkarni. 2020-06-16. End-to-End Code Switching Language Models for Automatic Speech Recognition. https://arxiv.org/abs/2006.08870

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