Papers by Ho-Young Jung
Contrastive and Consistency Learning for Neural Noisy-Channel Model in Spoken Language Understanding (2024.naacl-long)
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| Challenge: | End-to-end learning models require large volume of speech data with intent labels . however, models are sensitive to inconsistencies between training and evaluation conditions . |
| Approach: | They propose a module-based approach to learn intent in a noisy-channel model . they correlate error patterns between clean and noisy ASR transcripts . |
| Outcome: | The proposed method outperforms existing methods and improves in noisy environments. |