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We propose a) a Language Agnostic end-to-end Speech Translation model (LAST),and b) a data augmentation strategy to increase code-switching (CS)performance. With increasing globalization, multiple languages are increasinglyused interchangeably during fluent speech. Such CS complicates traditionalspeech recognition and translation, as we must recognize which language wasspoken first and then apply a language-dependent recognizer and subsequenttranslation component to generate the desired target language output. Such apipeline introduces latency and errors. In this paper, we eliminate the needfor that, by treating speech recognition and translation as one unifiedend-to-end speech translation problem. By training LAST with both inputlanguages, we decode speech into one target language, regardless of the inputlanguage. LAST delivers comparable recognition and speech translation accuracyin monolingual usage, while reducing latency and error rate considerably whenCS is observed.

Code-Switching without Switching: Language Agnostic End-to-End Speech Translation