Improving Low Resource Code-switched ASR using Augmented Code-switched\n TTS
Journal: arXiv (Cornell University)
Year: 2020

Building Automatic Speech Recognition (ASR) systems for code-switched speech\nhas recently gained renewed attention due to the widespread use of speech\ntechnologies in multilingual communities worldwide. End-to-end ASR systems are\na natural modeling choice due to their ease of use and superior performance in\nmonolingual settings. However, it is well known that end-to-end systems require\nlarge amounts of labeled speech. In this work, we investigate improving\ncode-switched ASR in low resource