Papers by Miaomiao Li
CB-Whisper: Contextual Biasing Whisper Using Open-Vocabulary Keyword-Spotting (2024.lrec-main)
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Yuang Li, Yinglu Li, Min Zhang, Chang Su, Jiawei Yu, Mengyao Piao, Xiaosong Qiao, Miaomiao Ma, Yanqing Zhao, Hao Yang
| Challenge: | End-to-end automatic speech recognition systems struggle to recognize rare name entities such as personal names, organizations and terminologies that are not frequently encountered in the training data. |
| Approach: | They propose a convolutional neural network-based ASR system that performs open-vocabulary keyword-spotting before the decoder to match the features between the entities and the utterances. |
| Outcome: | The proposed system significantly improves mixed-error-rate (MER) and entity recall compared to the original Whisper model on three internal datasets and two publicly available datasets. |
HGCN4MeSH: Hybrid Graph Convolution Network for MeSH Indexing (2020.acl-srw)
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| Challenge: | Recent deep learning methods for MeSH indexing fail to capture complex correlations between terms. |
| Approach: | They propose a model to learn the relationship between MeSH terms using Graph Convolution Network (GCN) they use two biGRUs to learn embedding representations of abstract and title of MeSH index text . |
| Outcome: | The proposed model is competitive with the state-of-the-art models on two datasets. |
Understanding and Mitigating Bias Inheritance in LLM-based Data Augmentation on Downstream Tasks (2026.acl-long)
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Miaomiao Li, Hao Chen, Yang Wang, Tingyuan Zhu, Weijia Zhang, Kaijie Zhu, Kam-Fai Wong, Jindong Wang
| Challenge: | Generating synthetic datasets via large language models (LLMs) has emerged as promising approach to improve LLM performance. |
| Approach: | They propose three mitigation strategies to mitigate bias inheritance in LLMs by analyzing real and LLM-augmented data. |
| Outcome: | The proposed methods can work differently on different tasks and biases. |
Cross-Domain Audio Deepfake Detection: Dataset and Analysis (2024.emnlp-main)
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| Challenge: | Existing audio deepfake detection datasets are outdated and lack generalization capabilities. |
| Approach: | They construct a new cross-domain audio deepfake detection dataset comprising over 300 hours of speech data that is generated by five advanced zero-shot TTS models. |
| Outcome: | The proposed models achieve 4.1% and 6.5% error rates in the cross-domain ADD dataset generated by five advanced zero-shot TTS models. |