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.
@article{arxiv.2006.08870,
title = {End-to-End Code Switching Language Models for Automatic Speech Recognition},
author = {Ahan M. R. and Shreyas Sunil Kulkarni},
journal= {arXiv preprint arXiv:2006.08870},
year = {2020}
}
Comments
5 pages, 2 figures, To appear in the proceedings of First Workshop on Speech Technologies for Code-switching in Multilingual Communities 2020