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.
Enhancing low-resource asr through versatile tts: Bridging the data gap,
2 Pith papers cite this work. Polarity classification is still indexing.
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Layer selection plus RIR augmentation on synthetic speech matches full real-data ASR performance using 25% real speech in SLAM-ASR.
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Improving Code-Switching ASR with Code-Mixing Guided Synthetic Speech
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.
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How to Leverage Synthetic Speech for LLM-Based ASR Systems?
Layer selection plus RIR augmentation on synthetic speech matches full real-data ASR performance using 25% real speech in SLAM-ASR.