Tara N. Sainath

29.4k total citations · 13 hit papers
180 papers, 17.5k citations indexed

About

Tara N. Sainath is a scholar working on Artificial Intelligence, Signal Processing and Computer Vision and Pattern Recognition. According to data from OpenAlex, Tara N. Sainath has authored 180 papers receiving a total of 17.5k indexed citations (citations by other indexed papers that have themselves been cited), including 165 papers in Artificial Intelligence, 117 papers in Signal Processing and 17 papers in Computer Vision and Pattern Recognition. Recurrent topics in Tara N. Sainath's work include Speech Recognition and Synthesis (144 papers), Speech and Audio Processing (103 papers) and Music and Audio Processing (83 papers). Tara N. Sainath is often cited by papers focused on Speech Recognition and Synthesis (144 papers), Speech and Audio Processing (103 papers) and Music and Audio Processing (83 papers). Tara N. Sainath collaborates with scholars based in United States, Canada and United Kingdom. Tara N. Sainath's co-authors include Brian Kingsbury, George E. Dahl, Geoffrey E. Hinton, Andrew Senior, Abdelrahman Mohamed, Navdeep Jaitly, Patrick Nguyen, Vincent Vanhoucke, Li Deng and Dong Yu and has published in prestigious journals such as IEEE Signal Processing Magazine, Neural Networks and IEEE Transactions on Parallel and Distributed Systems.

In The Last Decade

Tara N. Sainath

173 papers receiving 16.0k citations

Hit Papers

Deep Neural Networks for ... 2011 2026 2016 2021 2012 2012 2015 2013 2013 2.0k 4.0k 6.0k
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. Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
    2012 6.6k citations Geoffrey E. Hinton, Li Deng et al. IEEE Signal Processing Magazine profile →
  2. Deep Neural Networks for Acoustic Modeling in Speech Recognition
    2012 1.2k citations Geoffrey E. Hinton, Li Deng et al. IEEE Signal Processing Magazine profile →
  3. Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks
    2015 1.1k citations Tara N. Sainath, Oriol Vinyals et al. profile →
  4. Improving deep neural networks for LVCSR using rectified linear units and dropout
    2013 933 citations George E. Dahl, Tara N. Sainath et al. profile →
  5. Deep convolutional neural networks for LVCSR
    2013 723 citations Tara N. Sainath, Abdelrahman Mohamed et al. profile →
  6. Deep Learning for Audio Signal Processing
    2019 475 citations H.‐G. Purwins, Bo Li et al. IEEE Journal of Selected Topics in Signal Processing profile →
  7. Deep Convolutional Neural Networks for Large-scale Speech Tasks
    2014 364 citations Tara N. Sainath, Brian Kingsbury et al. profile →
  8. Streaming End-to-end Speech Recognition for Mobile Devices
    2019 345 citations Tara N. Sainath, Rohit Prabhavalkar et al. profile →
  9. Low-rank matrix factorization for Deep Neural Network training with high-dimensional output targets
    2013 344 citations Tara N. Sainath, Brian Kingsbury et al. profile →
  10. Convolutional neural networks for small-footprint keyword spotting
    2015 318 citations Tara N. Sainath, Carolina Parada profile →
  11. Learning the speech front-end with raw waveform CLDNNs
    2015 275 citations Tara N. Sainath, Andrew Senior et al. profile →
  12. Deep Belief Networks using discriminative features for phone recognition
    2011 196 citations Abdelrahman Mohamed, Tara N. Sainath et al. profile →
  13. Self-Supervised Speech Representation Learning: A Review
    2022 194 citations Hung-yi Lee, Tara N. Sainath et al. IEEE Journal of Selected Topics in Signal Processing profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tara N. Sainath United States 48 11.5k 7.6k 3.2k 1.4k 718 180 17.5k
George E. Dahl United States 21 9.8k 0.9× 5.5k 0.7× 3.8k 1.2× 1.5k 1.1× 750 1.0× 25 17.6k
Brian Kingsbury United States 38 9.3k 0.8× 6.0k 0.8× 2.7k 0.9× 1.1k 0.8× 527 0.7× 137 14.1k
Navdeep Jaitly United States 34 10.1k 0.9× 5.8k 0.8× 2.8k 0.9× 1.1k 0.8× 569 0.8× 62 16.7k
Abdelrahman Mohamed United States 29 13.1k 1.1× 7.7k 1.0× 4.5k 1.4× 2.0k 1.5× 1.0k 1.5× 52 21.6k
Andrew Senior United States 41 11.1k 1.0× 7.0k 0.9× 5.3k 1.7× 1.9k 1.4× 763 1.1× 89 21.7k
Biing‐Hwang Juang United States 37 8.5k 0.7× 6.7k 0.9× 3.6k 1.1× 3.1k 2.2× 810 1.1× 184 15.7k
Dong Yu United States 61 17.8k 1.5× 12.2k 1.6× 5.1k 1.6× 1.8k 1.3× 934 1.3× 375 26.9k
Kuldip K. Paliwal Australia 48 5.0k 0.4× 3.9k 0.5× 2.1k 0.7× 825 0.6× 463 0.6× 250 13.6k
Oriol Vinyals United States 33 9.1k 0.8× 3.3k 0.4× 7.8k 2.5× 854 0.6× 418 0.6× 74 16.4k
Sanjeev Khudanpur United States 45 14.8k 1.3× 8.6k 1.1× 2.0k 0.6× 451 0.3× 308 0.4× 269 17.9k

Countries citing papers authored by Tara N. Sainath

Since Specialization
Citations

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

Fields of papers citing papers by Tara N. Sainath

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tara N. Sainath. 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 Tara N. Sainath. The network helps show where Tara N. Sainath may publish in the future.

Co-authorship network of co-authors of Tara N. Sainath

This figure shows the co-authorship network connecting the top 25 collaborators of Tara N. Sainath. A scholar is included among the top collaborators of Tara N. Sainath 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 Tara N. Sainath. Tara N. Sainath 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.
Wang, Weiran, Rohit Prabhavalkar, Zhong Meng, et al.. (2024). Massive End-to-end Speech Recognition Models with Time Reduction. 6206–6217. 1 indexed citations
2.
3.
Kumar, Shankar, et al.. (2023). Semantic Segmentation with Bidirectional Language Models Improves Long-form ASR. 2778–2782. 1 indexed citations
4.
Meng, Zhong, Rohit Prabhavalkar, Andrew Rosenberg, et al.. (2023). Improving Joint Speech-Text Representations Without Alignment. 1354–1358. 1 indexed citations
5.
Yang, Chao-Han Huck, Bo Li, Yu Zhang, et al.. (2023). How to Estimate Model Transferability of Pre-Trained Speech Models?. 4 indexed citations
6.
Yu, Jiahui, Wei Han, Anmol Gulati, et al.. (2021). Dual-mode ASR: Unify and Improve Streaming ASR with Full-context Modeling. International Conference on Learning Representations. 5 indexed citations
7.
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
8.
Purwins, H.‐G., Bo Li, Tuomas Virtanen, et al.. (2019). Deep Learning for Audio Signal Processing. IEEE Journal of Selected Topics in Signal Processing. 13(2). 206–219. 475 indexed citations breakdown →
9.
Li, Bo, Yu Zhang, Tara N. Sainath, Yonghui Wu, & William Chan. (2019). Bytes Are All You Need: End-to-end Multilingual Speech Recognition and Synthesis with Bytes. 5621–5625. 89 indexed citations
10.
Pundak, Golan, Tara N. Sainath, Rohit Prabhavalkar, Anjuli Kannan, & Ding Zhao. (2018). Deep Context: End-to-end Contextual Speech Recognition. 418–425. 95 indexed citations
11.
Prabhavalkar, Rohit, Kanishka Rao, Tara N. Sainath, et al.. (2017). A Comparison of Sequence-to-Sequence Models for Speech Recognition. 939–943. 178 indexed citations
12.
Sainath, Tara N., et al.. (2016). Feature Learning with Raw-Waveform CLDNNs for Voice Activity Detection. Biblos-e Archivo (Universidad Autónoma de Madrid). 3668–3672. 78 indexed citations
13.
Liao, Hank, Golan Pundak, Olivier Siohan, et al.. (2015). Large vocabulary automatic speech recognition for children. 1611–1615. 76 indexed citations
14.
Sainath, Tara N. & Carolina Parada. (2015). Convolutional neural networks for small-footprint keyword spotting. 1478–1482. 318 indexed citations breakdown →
15.
Sainath, Tara N., Oriol Vinyals, Andrew Senior, & Haşim Sak. (2015). Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks. 4580–4584. 1080 indexed citations breakdown →
16.
andrew, Andrew, Haşim Sak, Félix de Chaumont Quitry, Tara N. Sainath, & Kanishka Rao. (2015). Acoustic modelling with CD-CTC-SMBR LSTM RNNS. 604–609. 61 indexed citations
17.
Soltau, Hagen, George Saon, & Tara N. Sainath. (2014). Joint training of convolutional and non-convolutional neural networks. 5572–5576. 50 indexed citations
18.
Hinton, Geoffrey E., Li Deng, Dong Yu, et al.. (2012). Deep Neural Networks for Acoustic Modeling in Speech Recognition. IEEE Signal Processing Magazine. 29(6). 82–97. 1169 indexed citations breakdown →
19.
Sainath, Tara N., Brian Kingsbury, & Bhuvana Ramabhadran. (2012). Auto-encoder bottleneck features using deep belief networks. 4153–4156. 138 indexed citations
20.
Sainath, Tara N., et al.. (2012). N-best entropy based data selection for acoustic modeling. 40. 4133–4136. 17 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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