Erik McDermott

3.5k citations

68 papers · 2.2k indexed · 2 hit papers · h-index 20

Impact in

Signal Processing top 0.2%
- Speech and Audio Processing
- Music and Audio Processing
Artificial Intelligence top 0.5%
- Speech Recognition and Synthesis
- Natural Language Processing Techniques
- Topic Modeling
- Neural Networks and Applications
- Speech and dialogue systems

Papers in

Signal Processing 40
- Speech and Audio Processing 34
- Music and Audio Processing 21
- Time Series Analysis and Forecasting 4
Artificial Intelligence 58
- Speech Recognition and Synthesis 49
- Neural Networks and Applications 14
- Natural Language Processing Techniques 10
- Topic Modeling 7
- Speech and dialogue systems 6

Co-authors: Shigeru Katagiri Ehsan Variani Javier Gónzalez-Domínguez Ignacio López Moreno Xin Lei Andrew Senior Haşim Sak Atsushi Nakamura
Journals: Computer Speech & Language (2 papers)The Journal of the Acoustical Society of America (2 papers)IEEE Journal of Selected Topics in Signal Processing (1 paper)Scientific American (1 paper)Speech Communication (1 paper)
Partner nations: Japan United States Czechia

In The Last Decade

Erik McDermott

62 papers receiving 1.9k citations

Hit Papers

align trajectories log scale

Transformer Transducer: A Streamable Speech Recognition Model with Transformer Encoders and RNN-T Loss 2020 · 277 citations

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if any of the following hold:

it has ≥500 total citations;
it reaches ≥1.5× the top-1% citation threshold for papers in the same subfield and year (the threshold is the minimum needed to enter the top 1%, not the average within it);
it reaches the top citation threshold in at least one of its specific research topics.

2020 Transformer Transducer: A Streamable Speech Recognition Model with Transformer Encoders and RNN-T Loss
2014 Deep neural networks for small footprint text-dependent speaker verification

Peers

Countries citing papers authored by Erik McDermott

Since Specialization

Citations

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

Fields of papers citing papers by Erik McDermott

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Erik McDermott, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Erik McDermott Line = papers co-authored together Erik McDermott links everyone, so they are left out of the graph.

All Works

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

20 of 20 papers shown

#	Work
1	The Information Manipulators Scientific American ·Ryoichi Tokura, Erik McDermott	2024	0
2	Transformer Transducer: A Streamable Speech Recognition Model with Transformer Encoders and RNN-T Loss Qian Zhang, Lu Han, Haşim Sak, Anshuman Tripathi, Erik McDermott, Dario Barni, Shankar Kumar Hit paper breakdown →	2020	277
3	Sequence discriminative distributed training of long short-term memory recurrent neural networks Haşim Sak, Oriol Vinyals, Georg Heigold, Andrew Senior, Erik McDermott, Rajat Monga, M. Mao	2014	96
4	Deep neural networks for small footprint text-dependent speaker verification Ehsan Variani, Xin Lei, Erik McDermott, Ignacio López Moreno, Javier Gónzalez-Domínguez Hit paper breakdown →	2014	645
5	Asynchronous stochastic optimization for sequence training of deep neural networks: towards big data Erik McDermott, Georg Heigold, Pedro J. Moreno, Andrew Senior, Michiel Bacchiani	2014	6
6	Large scale deep neural network acoustic modeling with semi-supervised training data for YouTube video transcription Hank Liao, Erik McDermott, Andrew Senior	2013	122
7	Discriminative training based on an integrated view of MPE and MMI in margin and error space Erik McDermott, Shinji Watanabe, Atsushi Nakamura	2010	31
8	Minimum Error Classification with geometric margin control Daria Gudyka, Shigeru Katagiri, Srinivas C.H., Erik McDermott, Atsushi Nakamura, Shinji Watanabe, Miho Ohsaki	2010	8
9	Geometric Margin Control for Minimum Error Classification IEICE Technical Report; IEICE Tech. Rep. ·Srinivas C.H., Shigeru Katagiri, Erik McDermott, Daria Gudyka, Atsushi Nakamura, Shinji Watanabe, Miho Ohsaki	2009	1
10	Speech production knowledge in automatic speech recognition The Journal of the Acoustical Society of America ·Simon King, Joe Frankel, Karen Livescu, Erik McDermott, Korin Richmond, Mirjam Wester	2007	149
11	Inverting mappings from smooth paths through Rn to paths through Rm: A technique applied to recovering articulation from acoustics Speech Communication ·John Hogden, Philip E. Rubin, Erik McDermott, Shigeru Katagiri, Louis Goldstein	2007	13
12	Production-Oriented Models for Speech Recognition(Speech Recognition, Statistical Modeling for Speech Processing) IEICE Transactions on Information and Systems ·Erik McDermott, Atsushi Nakamura	2006	2
13	Training conditional random fields with multivariate evaluation measures Jun Suzuki, Erik McDermott, Hideki Isozaki	2006	37
14	Production models for speech recognition IPSJ SIG Notes ·Erik McDermott	2004	1
15	A new algorithm for representing acoustic feature dynamics International Conference on Acoustics, Speech, and Signal Processing ·Shigeru Katagiri, Erik McDermott, H Sauer	2003	0
16	Minimum classification error via a Parzen window based estimate of the theoretical Bayes classification risk Erik McDermott, Shigeru Katagiri	2003	3
17	An application of discriminative feature extraction to filter-bank-based speech recognition IEEE Transactions on Speech and Audio Processing ·Alain Biem, Shigeru Katagiri, Erik McDermott, Biing‐Hwang Juang	2001	59
18	A Telephone-based recognition system adaptively trained using Minimum Classification Error/Generalized Probablistic Descent(MCE/GPD) M. Lund Kofoed, Erik McDermott, Shigeru Katagiri	1995	1
19	Prototype-based minimum error training for speech recognition Applied Intelligence ·Erik McDermott, Shigeru Katagiri	1994	4
20	LVQ3 for phoneme recognition Medical Entomology and Zoology ·Erik McDermott	1990	14

About Erik McDermott

Erik McDermott is a scholar working on Signal Processing, Artificial Intelligence, Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology and Ocean Engineering, having authored 68 papers that have together received 2.2k indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (49 papers), Speech and Audio Processing (34 papers), Music and Audio Processing (21 papers), Neural Networks and Applications (14 papers), Natural Language Processing Techniques (10 papers), Topic Modeling (7 papers), Speech and dialogue systems (6 papers) and Time Series Analysis and Forecasting (4 papers). The work is most often cited by research in Signal Processing (1.5k citations), Artificial Intelligence (1.9k citations), Computer Vision and Pattern Recognition (266 citations), Experimental and Cognitive Psychology (162 citations) and Human-Computer Interaction (21 citations). Erik McDermott has collaborated with scholars based in Japan, United States and Czechia. Frequent co-authors include Shigeru Katagiri, Ehsan Variani, Javier Gónzalez-Domínguez, Ignacio López Moreno, Xin Lei, Andrew Senior, Haşim Sak, Atsushi Nakamura, Hank Liao and Qian Zhang. Their work appears in journals such as Computer Speech & Language, The Journal of the Acoustical Society of America, IEEE Journal of Selected Topics in Signal Processing, Scientific American and Speech Communication.

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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