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Code-Switching (CS) multilingual Automatic Speech Recognition (ASR) modelscan transcribe speech containing two or more alternating languages during aconversation. This paper proposes (1) a new method for creating code-switchingASR datasets from purely monolingual data sources, and (2) a novel ConcatenatedTokenizer that enables ASR models to generate language ID for each emitted texttoken while reusing existing monolingual tokenizers. The efficacy of theseapproaches for building CS ASR models is demonstrated for two language pairs,English-Hindi and English-Spanish, where we achieve new state-of-the-artresults on the Miami Bangor CS evaluation corpus. In addition to competitiveASR performance, the proposed Concatenated Tokenizer models are highlyeffective for spoken language identification, achieving 98%+ accuracy on theout-of-distribution FLEURS dataset.

Unified model for code-switching speech recognition and language identification based on a concatenated tokenizer