Simon King

11.9k total citations · 2 hit papers
311 papers, 8.2k citations indexed

About

Simon King is a scholar working on Artificial Intelligence, Signal Processing and Experimental and Cognitive Psychology. According to data from OpenAlex, Simon King has authored 311 papers receiving a total of 8.2k indexed citations (citations by other indexed papers that have themselves been cited), including 245 papers in Artificial Intelligence, 152 papers in Signal Processing and 39 papers in Experimental and Cognitive Psychology. Recurrent topics in Simon King's work include Speech Recognition and Synthesis (226 papers), Speech and Audio Processing (133 papers) and Natural Language Processing Techniques (90 papers). Simon King is often cited by papers focused on Speech Recognition and Synthesis (226 papers), Speech and Audio Processing (133 papers) and Natural Language Processing Techniques (90 papers). Simon King collaborates with scholars based in United Kingdom, United States and Japan. Simon King's co-authors include Junichi Yamagishi, Joe Frankel, Zhizheng Wu, Oliver Watts, Karen Livescu, Mathew Magimai.-Doss, Özgür Çetin, Nash Borges, Xuemin Chi and Luc Lavoie and has published in prestigious journals such as Environmental Science & Technology, Water Research and The Journal of the Acoustical Society of America.

In The Last Decade

Simon King

305 papers receiving 7.5k citations

Hit Papers

align trajectories

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.

Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2007 (ICASSP 2007)

2007 1.6k citations Joe Frankel, Simon King et al. profile →
An Overview of Voice Conversion and Its Challenges: From Statistical Modeling to Deep Learning

2020 224 citations Junichi Yamagishi, Simon King et al. profile →

Peers

Simon King

AI

SP

ECP

CVPR

CN

Steve Renals United Kingdom

Lawrence R. Rabiner United States

Sanjeev Khudanpur United States

Alex Acero United States

Daniel Povey United States

Shinji Watanabe United States

Karen Livescu United States

Alex Waibel United States

Mike Schuster United States

Brian Kingsbury United States

Steve Renals United Kingdom

Simon King

6.3k ×1.2

AI

3.8k ×0.9

SP

811 ×0.7

ECP

1.1k ×0.9

CVPR

321 ×0.6

CN

Citations per year, relative to Simon King Simon King (= 1×) peers Steve Renals

Countries citing papers authored by Simon King

Since Specialization

Citations

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

Fields of papers citing papers by Simon King

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Simon King

This figure shows the co-authorship network connecting the top 25 collaborators of Simon King. A scholar is included among the top collaborators of Simon King 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 Simon King. Simon King is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Lai, Catherine, et al.. (2024). Hierarchical Intonation Modelling for Speech Synthesis using Legendre Polynomial Coefficients. Edinburgh Research Explorer. 1030–1034. 2 indexed citations

2.

Bailly, Gérard, et al.. (2024). Refining the evaluation of speech synthesis: A summary of the Blizzard Challenge 2023. Computer Speech & Language. 90. 101747–101747. 6 indexed citations

3.

Merritt, Thomas, Robert A. Clark, Zhizheng Wu, Junichi Yamagishi, & Simon King. (2016). Deep neural network-guided unit selection synthesis. Edinburgh Research Explorer. 5145–5149. 29 indexed citations

4.

Watts, Oliver, et al.. (2015). Combining Lightly-supervised Learning and User Feedback to Construct Andimprove a Statistical Parametric Speech Synthesizer for Malay. Research Journal of Applied Sciences Engineering and Technology. 11(11). 1227–1232. 1 indexed citations

5.

King, Simon, et al.. (2014). A perceptually-motivated low-complexity instantaneous linear channel normalization technique applied to speaker verification. Computer Speech & Language. 31(1). 1–27. 6 indexed citations

6.

King, Simon. (2014). Measuring a decade of progress in Text-to-Speech. Edinburgh Research Explorer (University of Edinburgh). 1(1). e006–e006. 61 indexed citations

7.

Prahallad, Kishore, et al.. (2013). The Blizzard Challenge 2013 -- Indian Language Task. 9 indexed citations

8.

Veaux, Christophe, Junichi Yamagishi, & Simon King. (2013). SLPAT 2013, 4th Workshop on Speech and Language Processing for Assistive Technologies. 2 indexed citations

9.

Watts, Oliver, et al.. (2013). Unsupervised and lightly-supervised learning for rapid construction of TTS systems in multiple languages from 'found' data: evaluation and analysis. Edinburgh Research Explorer. 101–106. 23 indexed citations

10.

Merritt, Thomas & Simon King. (2013). Investigating the shortcomings of HMM synthesis. SSW. 165–170. 8 indexed citations

11.

King, Simon & Andrew Curtis. (2012). Suppressing nonphysical reflections in Green’s function estimates using source-receiver interferometry. Geophysics. 77(1). Q15–Q25. 46 indexed citations

12.

King, Simon & Andrew Curtis. (2011). Velocity analysis using both reflections and refractions in seismic interferometry. Geophysics. 76(5). SA83–SA96. 12 indexed citations

13.

Kurimo, Mikko, Bill Byrne, John Dines, et al.. (2010). Personalising Speech-To-Speech Translation in the EMIME Project. ERA. 48–53. 15 indexed citations

14.

Kauffman, Louis H., Simon King, Vassily Olegovich Manturov, & Józef H. Przytycki. (2009). Invariants in Low-Dimensional Topology. Oberwolfach Reports. 5(2). 1157–1232. 1 indexed citations

15.

Yamagishi, Junichi, et al.. (2009). Analysis of Unsupervised and Noise-Robust Speaker-Adaptive HMM-Based Speech Synthesis Systems toward a Unified ASR and TTS Framework. Edinburgh Research Explorer (University of Edinburgh). 49–56. 7 indexed citations

16.

Yamagishi, Junichi, Bela Usabaev, Simon King, et al.. (2009). The 10th Annual Conference of the International Speech Communication Association, INTERSPEECH 2009. 21 indexed citations

17.

Aylett, Matthew P. & Simon King. (2008). Single Speaker Segmentation and Inventory Selection Using Dynamic Time Warping Self Organization and Joint Multigram Mapping. ERA. 258–263. 1 indexed citations

18.

Frankel, Joe, Dong Wang, & Simon King. (2008). Growing bottleneck features for tandem ASR. ERA. 1549. 7 indexed citations

19.

Shiga, Yoshinori & Simon King. (2004). Accurate Spectral Envelope Estimation for Articulation-to-Speech Synthesis. ERA. 19–24. 5 indexed citations

20.

Clark, Robert A., Korin Richmond, & Simon King. (2004). Festival 2 - build your own general purpose unit selection speech synthesiser.. ERA. 173–178. 36 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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