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

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Document pages: 5 pages

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

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