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Code-Switching (CS) remains a challenge for Automatic Speech Recognition(ASR), especially character-based models. With the combined choice ofcharacters from multiple languages, the outcome from character-based modelssuffers from phoneme duplication, resulting in language-inconsistent spellings.We propose Contextualized Connectionist Temporal Classification (CCTC) loss toencourage spelling consistencies of a character-based non-autoregressive ASRwhich allows for faster inference. The CCTC loss conditions the main predictionon the predicted contexts to ensure language consistency in the spellings. Incontrast to existing CTC-based approaches, CCTC loss does not requireframe-level alignments, since the context ground truth is obtained from themodel's estimated path. Compared to the same model trained with regular CTCloss, our method consistently improved the ASR performance on both CS andmonolingual corpora.

Reducing Spelling Inconsistencies in Code-Switching ASR using Contextualized CTC Loss