Papers by Hwa-Yeon Kim
Fast Bilingual Grapheme-To-Phoneme Conversion (2022.naacl-industry)
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| Challenge: | Autoregressive transformers (ART)-based grapheme-to-phoneme (G2P) models have been proposed for bi/multilingual text-to speech systems. |
| Approach: | They propose a bilingual grapheme-to-phoneme (G2P) model with autoregressive transformers for fast and exact decoding and data augmentation for predicting output length. |
| Outcome: | The proposed model achieves better performance than the previous model and 2700% faster inference speed. |
Data Augmentation by Data Noising for Open-vocabulary Slots in Spoken Language Understanding (N19-3)
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| Challenge: | Neural networks are used to understand spoken language understanding (SLU) but it is difficult to recognize the slots of unknown words or ‘open-vocabulary’ slots because of the high cost of creating a manually tagged SLU dataset. |
| Approach: | They propose to use a recurrent neural network to nois slots for data augmentation by using an attention-based bi-directional recurrence neural network. |
| Outcome: | The proposed method achieves performance improvements of up to 0.57% and 3.25 in intent prediction (accuracy) and slot filling (f1-score) and 0.53% accuracy. |