Anton Ragni

1.2k total citations
50 papers, 739 citations indexed

About

Anton Ragni is a scholar working on Artificial Intelligence, Signal Processing and Software. According to data from OpenAlex, Anton Ragni has authored 50 papers receiving a total of 739 indexed citations (citations by other indexed papers that have themselves been cited), including 46 papers in Artificial Intelligence, 25 papers in Signal Processing and 3 papers in Software. Recurrent topics in Anton Ragni's work include Speech Recognition and Synthesis (40 papers), Natural Language Processing Techniques (22 papers) and Speech and Audio Processing (21 papers). Anton Ragni is often cited by papers focused on Speech Recognition and Synthesis (40 papers), Natural Language Processing Techniques (22 papers) and Speech and Audio Processing (21 papers). Anton Ragni collaborates with scholars based in United Kingdom, United States and Hong Kong. Anton Ragni's co-authors include Mark Gales, Kate Knill, Shakti P. Rath, K.M. Knill, Yizhou Wang, Haipeng Wang, Takuya Yoshioka, Philip C. Woodland, Qi Li and Andrey Malinin and has published in prestigious journals such as IEEE Signal Processing Letters, IEEE/ACM Transactions on Audio Speech and Language Processing and Edinburgh Research Explorer (University of Edinburgh).

In The Last Decade

Anton Ragni

46 papers receiving 642 citations

Peers

Anton Ragni
Hainan Xu United States
Kate Knill United Kingdom
Yuya Unno United States
Adithya Renduchintala United States
Masao Someki United States
Vitaly Lavrukhin United States
Yuan Shangguan United States
Matt Shannon United Kingdom
Xingyu Na China
Suwon Shon United States
Hainan Xu United States
Anton Ragni
Citations per year, relative to Anton Ragni Anton Ragni (= 1×) peers Hainan Xu

Countries citing papers authored by Anton Ragni

Since Specialization
Citations

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

Fields of papers citing papers by Anton Ragni

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Anton Ragni

This figure shows the co-authorship network connecting the top 25 collaborators of Anton Ragni. A scholar is included among the top collaborators of Anton Ragni 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 Anton Ragni. Anton Ragni 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.
Ragni, Anton, et al.. (2024). How Much Context Does My Attention-Based ASR System Need?. 217–221.
3.
Ragni, Anton, et al.. (2024). Learning from memory-based models. 2360–2364.
4.
Ragni, Anton, et al.. (2023). Adapting Pretrained Models for Adult to Child Voice Conversion. White Rose Research Online (University of Leeds, The University of Sheffield, University of York). 271–275. 1 indexed citations
5.
Li, Yizhi, et al.. (2022). HERB: Measuring Hierarchical Regional Bias in Pre-trained Language Models. 334–346. 1 indexed citations
6.
Liu, Xunying, et al.. (2019). Exploiting Future Word Contexts in Neural Network Language Models for Speech Recognition. IEEE/ACM Transactions on Audio Speech and Language Processing. 27(9). 1444–1454. 17 indexed citations
7.
Knill, Kate, Mark Gales, Konstantinos G. Kyriakopoulos, et al.. (2018). Impact of ASR Performance on Free Speaking Language Assessment. Apollo (University of Cambridge). 1641–1645. 16 indexed citations
8.
Ragni, Anton, Qi Li, Mark Gales, & Yizhou Wang. (2018). Confidence Estimation and Deletion Prediction Using Bidirectional Recurrent Neural Networks. 204–211. 14 indexed citations
9.
Malinin, Andrey, Anton Ragni, Kate Knill, & Mark Gales. (2017). Incorporating Uncertainty into Deep Learning for Spoken Language Assessment. Apollo (University of Cambridge). 45–50. 17 indexed citations
10.
Ragni, Anton, et al.. (2017). Morph-to-word transduction for accurate and efficient automatic speech recognition and keyword search. Apollo (University of Cambridge). 5770–5774. 3 indexed citations
11.
Ragni, Anton, et al.. (2016). Multi-Language Neural Network Language Models. Apollo (University of Cambridge). 3042–3046. 6 indexed citations
12.
Ragni, Anton, et al.. (2016). Log-Linear System Combination Using Structured Support Vector Machines. Apollo (University of Cambridge). 1898–1902. 2 indexed citations
13.
Yang, Junwei, C. Zhang, Anton Ragni, Mark Gales, & Philip C. Woodland. (2016). System combination with log-linear models. 3. 5675–5679. 5 indexed citations
14.
Yang, Junwei, et al.. (2015). Structured discriminative models using deep neural-network features. 160–166. 3 indexed citations
15.
Cooper, Erica, Víctor Soto, Julia Hirschberg, et al.. (2015). Improving speech recognition and keyword search for low resource languages using web data. 829–833. 26 indexed citations
16.
Wang, Haipeng, et al.. (2015). Joint decoding of tandem and hybrid systems for improved keyword spotting on low resource languages. Apollo (University of Cambridge). 3660–3664. 37 indexed citations
17.
Ragni, Anton, Kate Knill, Shakti P. Rath, & Mark Gales. (2014). Data augmentation for low resource languages. Apollo (University of Cambridge). 810–814. 81 indexed citations
18.
Gales, Mark, Kate Knill, Anton Ragni, & Shakti P. Rath. (2014). Speech recognition and keyword spotting for low-resource languages : Babel project research at CUED. White Rose Research Online (University of Leeds, The University of Sheffield, University of York). 16–23. 79 indexed citations
19.
Ragni, Anton & Mark Gales. (2011). Derivative kernels for noise robust ASR. 119–124. 16 indexed citations
20.
Ragni, Anton & Mark Gales. (2011). Structured discriminative models for noise robust continuous speech recognition. 4788–4791. 16 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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