Multilingual Automatic Speech Recognition (ASR) models are capable of transcribing audios across multiple languages, eliminating the need for separate models. In addition, they can perform Language Identification (LID) and handle code-switched speech. However, training these models requires special code-switch and multilingual speech corpora which are sparsely available. In this paper, we evaluate different approaches towards training of bilingual as well as code-switched ASR models using purely monolingual data sources. We introduce the concept of aggregate tokenizers that differs from the current prevalent technique of generating LIDs at the boundaries of monolingual samples and produces LID for each emitted token instead. We compare bilingual and monolingual model performance, showcase the efficacy of aggregate tokenizers, present a synthetic code-switched ASR data generation technique and demonstrate the effectiveness of the proposed code-switched ASR models for the tasks of speech recognition and spoken language identification.
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