Ankur Bapna

5.1k total citations
23 papers, 729 citations indexed

About

Ankur Bapna is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Ankur Bapna has authored 23 papers receiving a total of 729 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Artificial Intelligence, 5 papers in Computer Vision and Pattern Recognition and 4 papers in Signal Processing. Recurrent topics in Ankur Bapna's work include Natural Language Processing Techniques (16 papers), Speech Recognition and Synthesis (14 papers) and Topic Modeling (14 papers). Ankur Bapna is often cited by papers focused on Natural Language Processing Techniques (16 papers), Speech Recognition and Synthesis (14 papers) and Topic Modeling (14 papers). Ankur Bapna collaborates with scholars based in United States, United Kingdom and Japan. Ankur Bapna's co-authors include Orhan Fırat, Yonghui Wu, Bhuvana Ramabhadran, Tara N. Sainath, Yu Zhang, Yuan Cao, Sneha Kudugunta, Isaac Caswell, Heiga Zen and Eugene Weinstein and has published in prestigious journals such as Zurich Open Repository and Archive (University of Zurich), Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) and ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

In The Last Decade

Ankur Bapna

22 papers receiving 657 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ankur Bapna United States 12 679 179 173 8 7 23 729
Zejun Ma China 11 273 0.4× 103 0.6× 289 1.7× 3 0.4× 5 0.7× 42 423
Mike Seltzer United States 7 265 0.4× 45 0.3× 196 1.1× 6 0.8× 7 1.0× 17 311
Enrico Bocchieri United States 13 628 0.9× 129 0.7× 420 2.4× 7 0.9× 13 1.9× 36 700
Adam Polyak Israel 10 344 0.5× 123 0.7× 157 0.9× 3 0.4× 2 0.3× 13 431
Hainan Xu United States 10 547 0.8× 46 0.3× 298 1.7× 7 0.9× 11 1.6× 22 585
Klaus Macherey Germany 9 534 0.8× 57 0.3× 192 1.1× 3 0.4× 16 2.3× 13 550
Vitaly Lavrukhin United States 6 312 0.5× 44 0.2× 214 1.2× 7 0.9× 6 0.9× 20 371
Kofi Boakye United States 8 204 0.3× 48 0.3× 148 0.9× 7 0.9× 9 1.3× 16 259
Stavros Tsakalidis United States 16 545 0.8× 208 1.2× 351 2.0× 2 0.3× 8 1.1× 42 705
Roberto Gemello Italy 10 396 0.6× 42 0.2× 299 1.7× 5 0.6× 14 2.0× 50 455

Countries citing papers authored by Ankur Bapna

Since Specialization
Citations

This map shows the geographic impact of Ankur Bapna'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 Ankur Bapna with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ankur Bapna more than expected).

Fields of papers citing papers by Ankur Bapna

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ankur Bapna. 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 Ankur Bapna. The network helps show where Ankur Bapna may publish in the future.

Co-authorship network of co-authors of Ankur Bapna

This figure shows the co-authorship network connecting the top 25 collaborators of Ankur Bapna. A scholar is included among the top collaborators of Ankur Bapna 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 Ankur Bapna. Ankur Bapna 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.
Ma, Min, Shikhar Vashishth, Ankur Bapna, et al.. (2024). Multimodal Modeling for Spoken Language Identification. 11526–11530.
2.
Koizumi, Yuma, Heiga Zen, Shigeki Karita, et al.. (2023). LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpus. 5496–5500. 31 indexed citations
3.
Wang, Gary, Kyle Kastner, Ankur Bapna, et al.. (2023). Understanding Shared Speech-Text Representations. 9. 1–5. 3 indexed citations
4.
Sainath, Tara N., Rohit Prabhavalkar, Ankur Bapna, et al.. (2023). JOIST: A Joint Speech and Text Streaming Model for ASR. 52–59. 16 indexed citations
5.
Zen, Heiga, Zhehuai Chen, Nobuyuki Morioka, et al.. (2023). Virtuoso: Massive Multilingual Speech-Text Joint Semi-Supervised Learning for Text-to-Speech. 1–5. 6 indexed citations
6.
Vashishth, Shikhar, Sriram Ganapathy, Ankur Bapna, et al.. (2023). Label Aware Speech Representation Learning For Language Identification. 5351–5355. 4 indexed citations
7.
Chen, Zhehuai, Ankur Bapna, Andrew Rosenberg, et al.. (2023). Maestro-U: Leveraging Joint Speech-Text Representation Learning for Zero Supervised Speech ASR. 68–75. 9 indexed citations
8.
Conneau, Alexis, Min Ma, Simran Khanuja, et al.. (2023). FLEURS: FEW-Shot Learning Evaluation of Universal Representations of Speech. 798–805. 73 indexed citations
9.
Zhang, Biao, et al.. (2022). Multilingual Document-Level Translation Enables Zero-Shot Transfer From Sentences to Documents. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 4176–4192. 3 indexed citations
10.
Ye, Jia, et al.. (2022). Leveraging unsupervised and weakly-supervised data to improve direct speech-to-speech translation. Interspeech 2022. 1721–1725. 11 indexed citations
11.
Cheng, Yong, Ankur Bapna, Orhan Fırat, et al.. (2022). Multilingual Mix: Example Interpolation Improves Multilingual Neural Machine Translation. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 4092–4102. 7 indexed citations
12.
Chen, Zhehuai, Zhang Yu, Andrew E. Rosenberg, et al.. (2022). MAESTRO: Matched Speech Text Representations through Modality Matching. Interspeech 2022. 54 indexed citations
13.
Bai, Junwen, Bo Li, Yu Zhang, et al.. (2022). Joint Unsupervised and Supervised Training for Multilingual ASR. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 6402–6406. 33 indexed citations
14.
Zhang, Biao, Ankur Bapna, Rico Sennrich, & Orhan Fırat. (2021). Share or Not? Learning to Schedule Language-Specific Capacity for Multilingual Translation. Zurich Open Repository and Archive (University of Zurich). 28 indexed citations
15.
Kudugunta, Sneha, Yanping Huang, Ankur Bapna, et al.. (2021). Exploring Routing Strategies for Multilingual Mixture-of-Experts Models. 1 indexed citations
16.
Kannan, Anjuli, Tara N. Sainath, Eugene Weinstein, et al.. (2019). Large-Scale Multilingual Speech Recognition with a Streaming End-to-End Model. 2130–2134. 107 indexed citations
17.
Bapna, Ankur & Orhan Fırat. (2019). Simple, Scalable Adaptation for Neural Machine Translation. 1538–1548. 172 indexed citations
18.
Kudugunta, Sneha, Ankur Bapna, Isaac Caswell, & Orhan Fırat. (2019). Investigating Multilingual NMT Representations at Scale. 51 indexed citations
19.
Bapna, Ankur, et al.. (2018). Training Deeper Neural Machine Translation Models with Transparent Attention. 3028–3033. 67 indexed citations
20.
Bapna, Ankur, Gökhan Tür, Dilek Hakkani‐Tür, & Larry Heck. (2017). Sequential Dialogue Context Modeling for Spoken Language Understanding. 103–114. 23 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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