  Code-Switching (CS) multilingual Automatic Speech Recognition (ASR) models
can transcribe speech containing two or more alternating languages during a
conversation. This paper proposes (1) a new method for creating code-switching
ASR datasets from purely monolingual data sources, and (2) a novel Concatenated
Tokenizer that enables ASR models to generate language ID for each emitted text
token while reusing existing monolingual tokenizers. The efficacy of these
approaches for building CS ASR models is demonstrated for two language pairs,
English-Hindi and English-Spanish, where we achieve new state-of-the-art
results on the Miami Bangor CS evaluation corpus. In addition to competitive
ASR performance, the proposed Concatenated Tokenizer models are highly
effective for spoken language identification, achieving 98%+ accuracy on the
out-of-distribution FLEURS dataset.
