Matt Shannon

712 total citations
13 papers, 372 citations indexed

About

Matt Shannon is a scholar working on Artificial Intelligence, Signal Processing and Computer Vision and Pattern Recognition. According to data from OpenAlex, Matt Shannon has authored 13 papers receiving a total of 372 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 10 papers in Signal Processing and 1 paper in Computer Vision and Pattern Recognition. Recurrent topics in Matt Shannon's work include Speech Recognition and Synthesis (12 papers), Speech and Audio Processing (9 papers) and Natural Language Processing Techniques (8 papers). Matt Shannon is often cited by papers focused on Speech Recognition and Synthesis (12 papers), Speech and Audio Processing (9 papers) and Natural Language Processing Techniques (8 papers). Matt Shannon collaborates with scholars based in United Kingdom, Japan and Switzerland. Matt Shannon's co-authors include Bill Byrne, Haşim Sak, Heiga Zen, Françoise Beaufays, Kanishka Rao, Simon King, Gabor Simko, Shuo-Yiin Chang, Carolina Parada and K. K. Chin and has published in prestigious journals such as IEEE Transactions on Audio Speech and Language Processing, Edinburgh Research Explorer (University of Edinburgh) and Apollo (University of Cambridge).

In The Last Decade

Matt Shannon

13 papers receiving 315 citations

Peers

Countries citing papers authored by Matt Shannon

Since Specialization

Citations

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

Fields of papers citing papers by Matt Shannon

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Matt Shannon

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

All Works

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13 of 13 papers shown

#	Work	Indexed citations
1	Speaker Generation 2022 ·ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ·Daisy Stanton, Matt Shannon, Soroosh Mariooryad, RJ Skerry-Ryan, Eric Battenberg, Tom Bagby, David Kao	11
2	Recurrent Neural Aligner: An Encoder-Decoder Neural Network Model for Sequence to Sequence Mapping 2017 ·Haşim Sak, Matt Shannon, Kanishka Rao, Françoise Beaufays	81
3	Acoustic Modeling for Google Home 2017 ·Bo Li, Tara N. Sainath, Arun Narayanan, (unknown), Michiel Bacchiani, Ananya Misra, Izhak Shafran, Haşim Sak, Golan Pundak, K. K. Chin, Khe Chai Sim, Ron J. Weiss, Kevin Wilson, Ehsan Variani, Chanwoo Kim, Olivier Siohan, M. Weintraub, Erik McDermott, Richard C. Rose, Matt Shannon	100
4	Improved End-of-Query Detection for Streaming Speech Recognition 2017 ·Matt Shannon, Gabor Simko, Shuo-Yiin Chang, Carolina Parada	25
5	Measuring the perceptual effects of modelling assumptions in speech synthesis using stimuli constructed from repeated natural speech 2014 ·Edinburgh Research Explorer (University of Edinburgh) ·Gustav Eje Henter, Thomas Merritt, Matt Shannon, Catherine Mayo, Simon King	23
6	Fast, low-artifact speech synthesis considering global variance 2013 ·Edinburgh Research Explorer ·Matt Shannon, Bill Byrne	12
7	Autoregressive Models for Statistical Parametric Speech Synthesis 2012 ·IEEE Transactions on Audio Speech and Language Processing ·Matt Shannon, Heiga Zen, Bill Byrne	51
8	The effect of using normalized models in statistical speech synthesis 2011 ·Matt Shannon, Heiga Zen, Bill Byrne	13
9	Personalising Speech-To-Speech Translation in the EMIME Project 2010 ·ERA ·Mikko Kurimo, Bill Byrne, John Dines, Philip N. Garner, (unknown), (unknown), Teemu Hirsimäki, Reima Karhila, Simon King, Hui Liang, Keiichiro Oura, Lakshmi Babu Saheer, Matt Shannon, (unknown), Jilei Tian	15
10	Speaker adaptation and the evaluation of speaker similarity in the EMIME speech-to-speech translation project 2010 ·ERA ·Mirjam Wester, John Dines, (unknown), Hui Liang, Yi-Jian Wu, Lakshmi Babu Saheer, Simon King, Keiichiro Oura, Philip N. Garner, Bill Byrne, (unknown), Teemu Hirsimäki, Reima Karhila, Mikko Kurimo, Matt Shannon, Sayaka Shiota, Jilei Tian, Keiichi Tokuda, Junichi Yamagishi	19
11	Autoregressive clustering for HMM speech synthesis 2010 ·Apollo (University of Cambridge) ·Matt Shannon, Bill Byrne	3
12	A formulation of the autoregressive HMM for speech synthesis 2009 ·Apollo (University of Cambridge) ·Matt Shannon, Bill Byrne	9
13	Autoregressive HMMs for speech synthesis 2009 ·Apollo (University of Cambridge) ·Matt Shannon, Bill Byrne	10

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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