Daisy Stanton
Impact in
- Signal Processing top 5%
- Speech and Audio Processing
- Music and Audio Processing
- Artificial Intelligence top 5%
- Speech Recognition and Synthesis
- Natural Language Processing Techniques
- Topic Modeling
- Speech and dialogue systems
- Text Readability and Simplification
Papers in
-
- Speech Recognition and Synthesis 5
- Natural Language Processing Techniques 4
- Speech and dialogue systems 4
- Topic Modeling 2
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- Music and Audio Processing 2
- Speech and Audio Processing 1
- Co-authors
- RJ Skerry-Ryan (4 shared papers)Yuxuan Wang (3 shared papers)Rif A. Saurous (2 shared papers)Ying Xiao (2 shared papers)Peng Xu (1 shared paper)Richard Zens (1 shared paper)Eric Battenberg (2 shared papers)Joel Shor (1 shared paper)
- Journals
- ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (1 paper)arXiv (Cornell University) (1 paper)Empirical Methods in Natural Language Processing (1 paper)International Conference on Machine Learning (1 paper)
- Partner nations
- United StatesChina
In The Last Decade
Daisy Stanton
6 papers receiving 229 citations
Peers
Comparison fields: 5 of 18
- Signal Processing 142
- Artificial Intelligence 247
- Computer Vision and Pattern Recognition 22
- Experimental and Cognitive Psychology 10
- Cultural Studies 2
Countries citing papers authored by Daisy Stanton
This map shows the geographic impact of Daisy Stanton'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 Daisy Stanton with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daisy Stanton more than expected).
Fields of papers citing papers by Daisy Stanton
This network shows the impact of papers produced by Daisy Stanton. 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 Daisy Stanton. The network helps show where Daisy Stanton may publish in the future.
Co-authors
The 25 scholars most cited alongside Daisy Stanton, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Tacotron: A Fully End-to-End Text-To-Speech Synthesis Model. | 2017 | 86 |
| 2 | 2018 | 73 | |
| 3 | Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis | 2018 | 62 |
| 4 | A Systematic Comparison of Phrase Table Pruning Techniques | 2012 | 29 |
| 5 | 2022 | 11 | |
| 6 | 2015 | 4 |
About Daisy Stanton
Daisy Stanton is a scholar working on Artificial Intelligence, Signal Processing, Infectious Diseases, Organic Chemistry and Surgery, having authored 6 papers that have together received 265 indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (5 papers), Natural Language Processing Techniques (4 papers), Speech and dialogue systems (4 papers), Topic Modeling (2 papers), Music and Audio Processing (2 papers) and Speech and Audio Processing (1 paper). The work is most often cited by research in Signal Processing (142 citations), Artificial Intelligence (247 citations), Computer Vision and Pattern Recognition (22 citations), Experimental and Cognitive Psychology (10 citations) and Cultural Studies (2 citations). Daisy Stanton has collaborated with scholars based in United States and China. Frequent co-authors include RJ Skerry-Ryan, Yuxuan Wang, Rif A. Saurous, Ying Xiao, Peng Xu, Richard Zens, Eric Battenberg, Joel Shor, Yannis Agiomyrgiannakis and Zhifeng Chen. Their work appears in journals such as ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), arXiv (Cornell University), Empirical Methods in Natural Language Processing and International Conference on Machine Learning.
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.