Changhan Wang

3.4k total citations · 1 hit paper
41 papers, 1.2k citations indexed

About

Changhan Wang is a scholar working on Artificial Intelligence, Signal Processing and Computer Vision and Pattern Recognition. According to data from OpenAlex, Changhan Wang has authored 41 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Artificial Intelligence, 5 papers in Signal Processing and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in Changhan Wang's work include Natural Language Processing Techniques (33 papers), Speech Recognition and Synthesis (25 papers) and Topic Modeling (19 papers). Changhan Wang is often cited by papers focused on Natural Language Processing Techniques (33 papers), Speech Recognition and Synthesis (25 papers) and Topic Modeling (19 papers). Changhan Wang collaborates with scholars based in United States, Israel and France. Changhan Wang's co-authors include Juan Pino, Jiatao Gu, Michael Auli, Alexei Baevski, Yun Tang, Alexis Conneau, Anne Wu, Xutai Ma, Douwe Kiela and Kyunghyun Cho and has published in prestigious journals such as Petroleum Science and Technology, arXiv (Cornell University) and Neural Information Processing Systems.

In The Last Decade

Changhan Wang

38 papers receiving 1.1k citations

Hit Papers

XLS-R: Self-supervised Cross-lingual Speech Representatio... 2022 2026 2023 2024 2022 50 100 150 200 250
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale
    2022 266 citations Arun Babu, Changhan Wang et al. Interspeech 2022 profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Changhan Wang United States 18 1.1k 280 161 42 35 41 1.2k
Xu Tan China 23 914 0.8× 398 1.4× 400 2.5× 24 0.6× 34 1.0× 61 1.1k
J. Fiscus United States 7 770 0.7× 473 1.7× 148 0.9× 21 0.5× 46 1.3× 11 901
Anthony Larcher France 12 776 0.7× 825 2.9× 195 1.2× 60 1.4× 21 0.6× 34 967
Jitendra Ajmera India 13 595 0.5× 601 2.1× 184 1.1× 43 1.0× 54 1.5× 32 824
Mikio Yamamoto Japan 11 541 0.5× 284 1.0× 66 0.4× 64 1.5× 23 0.7× 45 687
Yeshwant K. Muthusamy United States 12 638 0.6× 425 1.5× 92 0.6× 34 0.8× 66 1.9× 22 730
Ondřej Glembek Czechia 23 2.1k 1.9× 1.8k 6.4× 138 0.9× 30 0.7× 79 2.3× 57 2.2k
Ramón Fernández Astudillo Portugal 15 622 0.6× 269 1.0× 91 0.6× 36 0.9× 14 0.4× 55 721
Takehito Utsuro Japan 12 623 0.6× 131 0.5× 60 0.4× 70 1.7× 22 0.6× 130 732
Toshiyuki Takezawa Japan 15 860 0.8× 318 1.1× 72 0.4× 49 1.2× 39 1.1× 78 1.0k

Countries citing papers authored by Changhan Wang

Since Specialization
Citations

This map shows the geographic impact of Changhan Wang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Changhan Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Changhan Wang more than expected).

Fields of papers citing papers by Changhan Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Changhan Wang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Changhan Wang. The network helps show where Changhan Wang may publish in the future.

Co-authorship network of co-authors of Changhan Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Changhan Wang. A scholar is included among the top collaborators of Changhan Wang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Changhan Wang. Changhan Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Wang, Changhan, Hirofumi Inaguma, Peng‐Jen Chen, et al.. (2023). Simple and Effective Unsupervised Speech Translation. 10771–10784. 9 indexed citations
3.
Inaguma, Hirofumi, Ilia Kulikov, Peng‐Jen Chen, et al.. (2023). UnitY: Two-pass Direct Speech-to-speech Translation with Discrete Units. 15655–15680. 18 indexed citations
4.
Chen, Peng‐Jen, Kevin Tran, Yilin Yang, et al.. (2023). Speech-to-Speech Translation for a Real-world Unwritten Language. 4969–4983. 12 indexed citations
5.
Xu, Derek, Shuyan Dong, Changhan Wang, et al.. (2023). Introducing Semantics into Speech Encoders. 11413–11429.
6.
Gong, Hongyu, Ning Dong, Jingfei Du, et al.. (2023). SpeechMatrix: A Large-Scale Mined Corpus of Multilingual Speech-to-Speech Translations. SPIRE - Sciences Po Institutional REpository. 16251–16269. 11 indexed citations
7.
8.
Chen, Peng‐Jen, Changhan Wang, Juan Pino, et al.. (2022). Enhanced Direct Speech-to-Speech Translation Using Self-supervised Pre-training and Data Augmentation. Interspeech 2022. 5195–5199. 24 indexed citations
9.
Lee, Ann, Peng‐Jen Chen, Changhan Wang, et al.. (2022). Direct Speech-to-Speech Translation With Discrete Units. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 3327–3339. 63 indexed citations
10.
Babu, Arun, Changhan Wang, Andros Tjandra, et al.. (2022). XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale. Interspeech 2022. 2278–2282. 266 indexed citations breakdown →
11.
Wang, Changhan, Anne Wu, Jiatao Gu, & Juan Pino. (2021). CoVoST 2 and Massively Multilingual Speech Translation. 2247–2251. 50 indexed citations
12.
Li, Xian, Changhan Wang, Yun Tang, et al.. (2021). Multilingual Speech Translation from Efficient Finetuning of Pretrained Models. 827–838. 76 indexed citations
13.
Tran, Chau, Changhan Wang, Yuqing Tang, et al.. (2020). Cross-Modal Transfer Learning for Multilingual Speech-to-Text Translation.. arXiv (Cornell University). 2 indexed citations
14.
Wang, Changhan, Anne Wu, & Juan Pino. (2020). CoVoST 2: A Massively Multilingual Speech-to-Text Translation Corpus. arXiv (Cornell University). 27 indexed citations
15.
Wang, Changhan, et al.. (2020). Neural Machine Translation with Byte-Level Subwords. Proceedings of the AAAI Conference on Artificial Intelligence. 34(5). 9154–9160. 73 indexed citations
16.
Wu, Anne, Changhan Wang, Juan Pino, & Jiatao Gu. (2020). Self-Supervised Representations Improve End-to-End Speech Translation. 1491–1495. 24 indexed citations
17.
Gu, Jiatao, Changhan Wang, & Junbo Zhao. (2019). Levenshtein Transformer. Neural Information Processing Systems. 32. 11181–11191. 56 indexed citations
18.
DeVries, Terrance, Ishan Misra, Changhan Wang, & Laurens van der Maaten. (2019). Does Object Recognition Work for Everyone. Computer Vision and Pattern Recognition. 52–59. 12 indexed citations
19.
Kiela, Douwe, Changhan Wang, & Kyunghyun Cho. (2018). Context-Attentive Embeddings for Improved Sentence Representations. arXiv (Cornell University). 4 indexed citations
20.
Kiela, Douwe, Changhan Wang, & Kyunghyun Cho. (2018). Dynamic Meta-Embeddings for Improved Sentence Representations. 1466–1477. 57 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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