Vimal Manohar

2.3k total citations · 1 hit paper
28 papers, 1.1k citations indexed

About

Vimal Manohar is a scholar working on Artificial Intelligence, Signal Processing and Control and Systems Engineering. According to data from OpenAlex, Vimal Manohar has authored 28 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Artificial Intelligence, 17 papers in Signal Processing and 2 papers in Control and Systems Engineering. Recurrent topics in Vimal Manohar's work include Speech Recognition and Synthesis (26 papers), Music and Audio Processing (13 papers) and Speech and Audio Processing (12 papers). Vimal Manohar is often cited by papers focused on Speech Recognition and Synthesis (26 papers), Music and Audio Processing (13 papers) and Speech and Audio Processing (12 papers). Vimal Manohar collaborates with scholars based in United States, Greece and China. Vimal Manohar's co-authors include Sanjeev Khudanpur, Daniel Povey, Pegah Ghahremani, Vijayaditya Peddinti, Xingyu Na, Yiming Wang, Daniel Gálvez, Hossein Hadian, David Snyder and Shinji Watanabe and has published in prestigious journals such as IEEE/ACM Transactions on Audio Speech and Language Processing and Figshare.

In The Last Decade

Vimal Manohar

26 papers receiving 932 citations

Hit Papers

Purely Sequence-Trained Neural Networks for ASR Based on ... 2016 2026 2019 2022 2016 100 200 300 400
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. Purely Sequence-Trained Neural Networks for ASR Based on Lattice-Free MMI
    2016 458 citations Daniel Povey, Vijayaditya Peddinti et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Vimal Manohar United States 15 1.0k 749 49 48 28 28 1.1k
Xingyu Na China 5 902 0.9× 671 0.9× 63 1.3× 42 0.9× 25 0.9× 12 968
Matthew Wiesner United States 7 960 0.9× 621 0.8× 74 1.5× 49 1.0× 25 0.9× 37 1.0k
Pegah Ghahremani United States 12 1.1k 1.1× 901 1.2× 65 1.3× 136 2.8× 42 1.5× 18 1.3k
Tsubasa Ochiai Japan 7 897 0.9× 681 0.9× 71 1.4× 45 0.9× 26 0.9× 11 1.0k
Yuya Unno United States 4 789 0.8× 543 0.7× 65 1.3× 43 0.9× 24 0.9× 5 863
Adithya Renduchintala United States 6 855 0.8× 544 0.7× 89 1.8× 45 0.9× 24 0.9× 17 934
Hirofumi Inaguma Japan 15 913 0.9× 519 0.7× 81 1.7× 41 0.9× 17 0.6× 35 1.0k
Suyoun Kim United States 12 1.1k 1.1× 804 1.1× 103 2.1× 48 1.0× 12 0.4× 24 1.3k
Yanzhang He United States 15 829 0.8× 574 0.8× 71 1.4× 20 0.4× 12 0.4× 42 936
Christian Fuegen United States 12 691 0.7× 427 0.6× 54 1.1× 39 0.8× 12 0.4× 33 789

Countries citing papers authored by Vimal Manohar

Since Specialization
Citations

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

Fields of papers citing papers by Vimal Manohar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Vimal Manohar

This figure shows the co-authorship network connecting the top 25 collaborators of Vimal Manohar. A scholar is included among the top collaborators of Vimal Manohar 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 Vimal Manohar. Vimal Manohar 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.
Tjandra, Andros, et al.. (2023). Voice-Preserving Zero-Shot Multiple Accent Conversion. 1–5. 3 indexed citations
2.
Sarı, Leda, et al.. (2023). Self-Supervised Representations for Singing Voice Conversion. 1–5. 9 indexed citations
3.
Manohar, Vimal, et al.. (2019). Acoustic Modeling for Overlapping Speech Recognition: Jhu Chime-5 Challenge System. 6665–6669. 11 indexed citations
4.
Sell, Gregory, David Snyder, Alan McCree, et al.. (2018). Diarization is Hard: Some Experiences and Lessons Learned for the JHU Team in the Inaugural DIHARD Challenge. 2808–2812. 115 indexed citations
5.
Manohar, Vimal, Pegah Ghahremani, Daniel Povey, & Sanjeev Khudanpur. (2018). A Teacher-Student Learning Approach for Unsupervised Domain Adaptation of Sequence-Trained ASR Models. 4. 250–257. 32 indexed citations
6.
Manohar, Vimal, Hossein Hadian, Daniel Povey, & Sanjeev Khudanpur. (2018). Semi-Supervised Training of Acoustic Models Using Lattice-Free MMI. 4844–4848. 33 indexed citations
7.
8.
Kanda, Naoyuki, Rintaro Ikeshita, Shota Horiguchi, et al.. (2018). The Hitachi/JHU CHiME-5 system: Advances in speech recognition for everyday home environments using multiple microphone arrays. 6–10. 29 indexed citations
9.
Manohar, Vimal, Daniel Povey, & Sanjeev Khudanpur. (2017). JHU Kaldi system for Arabic MGB-3 ASR challenge using diarization, audio-transcript alignment and transfer learning. 346–352. 30 indexed citations
10.
Ghahremani, Pegah, Vimal Manohar, Hossein Hadian, Daniel Povey, & Sanjeev Khudanpur. (2017). Investigation of transfer learning for ASR using LF-MMI trained neural networks. 279–286. 40 indexed citations
11.
Trmal, Jan, Matthew Wiesner, Vijayaditya Peddinti, et al.. (2017). The Kaldi OpenKWS System: Improving Low Resource Keyword Search. 3597–3601. 22 indexed citations
12.
Zhang, Xiaohui, Vimal Manohar, Daniel Povey, & Sanjeev Khudanpur. (2017). Acoustic Data-Driven Lexicon Learning Based on a Greedy Pronunciation Selection Framework. 2541–2545. 3 indexed citations
13.
Povey, Daniel, Vijayaditya Peddinti, Daniel Gálvez, et al.. (2016). Purely Sequence-Trained Neural Networks for ASR Based on Lattice-Free MMI. 2751–2755. 458 indexed citations breakdown →
14.
Ghahremani, Pegah, Vimal Manohar, Daniel Povey, & Sanjeev Khudanpur. (2016). Acoustic Modelling from the Signal Domain Using CNNs. 3434–3438. 45 indexed citations
15.
Liu, Chunxi, Preethi Jyothi, Hao Tang, et al.. (2016). Adapting ASR for under-resourced languages using mismatched transcriptions. 5840–5844. 12 indexed citations
16.
Hasegawa‐Johnson, Mark, Preethi Jyothi, Daniel McCloy, et al.. (2016). ASR for Under-Resourced Languages From Probabilistic Transcription. IEEE/ACM Transactions on Audio Speech and Language Processing. 25(1). 50–63. 25 indexed citations
17.
Peddinti, Vijayaditya, Vimal Manohar, Yiming Wang, Daniel Povey, & Sanjeev Khudanpur. (2016). Far-Field ASR Without Parallel Data. 1996–2000. 20 indexed citations
18.
Manohar, Vimal, Daniel Povey, & Sanjeev Khudanpur. (2015). Semi-supervised maximum mutual information training of deep neural network acoustic models. 2630–2634. 23 indexed citations
19.
Trmal, Jan, Guoguo Chen, Sanjeev Khudanpur, et al.. (2014). A keyword search system using open source software. Figshare. 18. 530–535. 23 indexed citations
20.
Manohar, Vimal, et al.. (2013). Acoustic modeling using transform-based phone-cluster adaptive training. 49–54. 5 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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