Bilingual Automatic Speech Recognition

Bilingual automatic speech recognition (ASR) aims to build systems capable of accurately transcribing speech containing multiple languages, addressing challenges posed by code-switching (mixing languages within utterances) and monolingual segments. Current research focuses on optimizing model architectures like neural transducers and leveraging techniques such as attention mechanisms and byte-level subword representations to improve accuracy and efficiency, particularly in low-resource scenarios. These advancements are significant for improving human-computer interaction in multilingual settings and have implications for applications ranging from language learning tools to real-time translation services.