Papers by Xinjian Li
Enabling Real-time Neural IME with Incremental Vocabulary Selection (N19-2)
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| Challenge: | Input method editor (IME) converts sequential alphabet key inputs to words in a target language. |
| Approach: | They propose a neural-based language model that incrementally builds a subset vocabulary from the word lattice. |
| Outcome: | The proposed approach achieves 50x speedup on Japanese IME benchmark without losing conversion accuracy. |
Zero-shot Learning for Grapheme to Phoneme Conversion with Language Ensemble (2022.findings-acl)
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| Challenge: | Existing work focuses on low-resource and endangered languages with limited training sets. |
| Approach: | They propose a hypothesis set for any unseen target language and combine it with a confusion network to propose 'the most likely hypothesis' they test the approach on over 600 unseened languages and demonstrate it significantly outperforms baselines. |
| Outcome: | The proposed model outperforms baselines on over 600 unseen languages. |
Towards Robust Speech Representation Learning for Thousands of Languages (2024.emnlp-main)
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William Chen, Wangyou Zhang, Yifan Peng, Xinjian Li, Jinchuan Tian, Jiatong Shi, Xuankai Chang, Soumi Maiti, Karen Livescu, Shinji Watanabe
| Challenge: | XEUS is a cross-lingual encoder for universal speech that can be trained on 1 million hours of data across 4057 languages. |
| Approach: | They propose a Cross-lingual Encoder for Universal Speech that can be trained on 1 million hours of data across 4057 languages and a newly created corpus of 7400+ hours from 4057 . |
| Outcome: | The proposed model outperforms state-of-the-art models on several benchmarks and outperfies MMS 1B and w2v-BERT 2.0 v2 by 0.8% and 4.4% respectively. |
Phone Inventories and Recognition for Every Language (2022.lrec-1)
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| Challenge: | Identifying phone inventories is crucial component in language documentation and preservation of endangered languages. |
| Approach: | They propose a probabilistic and non-probabilistic phone inventory model that estimates the phone inventory for any language listed in Glottolog. |
| Outcome: | The proposed model outperforms baseline models by 6.5 F1 and improves the PER (phone error rate) in phone recognition by 25%. |
AlloVera: A Multilingual Allophone Database (2020.lrec-1)
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David R. Mortensen, Xinjian Li, Patrick Littell, Alexis Michaud, Shruti Rijhwani, Antonios Anastasopoulos, Alan W Black, Florian Metze, Graham Neubig
| Challenge: | Phonemes are contrastive phonological units, and allophones are their various concrete realizations. |
| Approach: | They propose a resource that maps allophones to phonemes for 14 languages . they propose phonological representations that are much closer to a universal transcription . |
| Outcome: | The proposed resource maps from 218 allophones to phonemes for 14 languages. |