A code-mixing guided preference-learning method for TTS produces synthetic data that lowers mixed error rate when fine-tuning Whisper on the SEAME Mandarin-English corpus.
Can we train asr systems on code- switch without real code-switch data? case study for singapore’s languages,
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Proposes Bayesian factorized adaptation for multilingual ASR to handle code-switching, reporting 32.87% fewer errors on switched words and 5.31% better overall WER while preserving monolingual accuracy with small synthetic data.
citing papers explorer
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Adding Robust Code-Switching Capabilities to High Performance Multilingual ASR
Proposes Bayesian factorized adaptation for multilingual ASR to handle code-switching, reporting 32.87% fewer errors on switched words and 5.31% better overall WER while preserving monolingual accuracy with small synthetic data.