World's Best Scientists 2026 revealed!
Douglas A. Reynolds

Douglas A. Reynolds

D-Index & Metrics

Computer Science

D-Index
68
Citations
37646
World Ranking
2021
National Ranking
1022

Research.com Recognitions

  • 2010 - IEEE Fellow For contributions to Gaussian-mixture-model techniques for automatic speaker recognition

Overview

Douglas A. Reynolds is a researcher affiliated with MIT in the United States, focusing primarily on areas within computer science. Their work spans several subfields including artificial intelligence and signal processing. The main topics featured in their research include speech recognition and synthesis, speech and audio processing, natural language processing techniques, music and audio processing, and wireless signal modulation classification.

Their frequent publication venues reflect a focus on audio and speech processing topics, with multiple papers in arXiv (Cornell University), and contributions to IEEE/ACM Transactions on Audio Speech and Language Processing and Computer Speech & Language.

Some of their recent papers include the following:

  • "Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification: Fundamentals" (2020), published in IEEE/ACM Transactions on Audio Speech and Language Processing
  • "VoxSRC 2020: The Second VoxCeleb Speaker Recognition Challenge" (2020), published in arXiv (Cornell University)
  • "Two decades into Speaker Recognition Evaluation - are we there yet?" (2020), published in Computer Speech & Language
  • "The 2021 NIST Speaker Recognition Evaluation" (2022), published in arXiv (Cornell University)
  • "The NIST CTS Speaker Recognition Challenge" (2022), published in arXiv (Cornell University)

Collaborations have been frequent with researchers across related domains. Prominent coauthors include Craig S. Greenberg, Elliot Singer, Lisa Reyes Mason, Kong Aik Lee, and Seyed Omid Sadjadi.

Douglas A. Reynolds has been recognized by the IEEE with the IEEE Fellow distinction awarded in 2010 for contributions to Gaussian-mixture-model techniques in automatic speaker recognition.

Best Publications

  • Speaker Verification Using Adapted Gaussian Mixture Models

    Douglas A. Reynolds;Thomas F. Quatieri;Robert B. Dunn

  • Robust text-independent speaker identification using Gaussian mixture speaker models

    D.A. Reynolds;R.C. Rose

  • Gaussian Mixture Models

    Unknown

  • Speaker identification and verification using Gaussian mixture speaker models

    Douglas A. Reynolds

  • Gaussian Mixture Models.

    Douglas A. Reynolds

  • Support vector machines using GMM supervectors for speaker verification

    W.M. Campbell;D.E. Sturim;D.A. Reynolds

  • A tutorial on text-independent speaker verification

    Frédéric Bimbot;Jean-François Bonastre;Corinne Fredouille;Guillaume Gravier

  • An overview of automatic speaker recognition technology

    Douglas A. Reynolds

  • SVM Based Speaker Verification using a GMM Supervector Kernel and NAP Variability Compensation

    W.M. Campbell;D.E. Sturim;D.A. Reynolds;A. Solomonoff

  • An overview of automatic speaker diarization systems

    S.E. Tranter;D.A. Reynolds

  • Support vector machines for speaker and language recognition

    William M. Campbell;Joseph P. Campbell;Douglas A. Reynolds;Elliot Singer

  • Approaches to Language Identification using Gaussian Mixture Models and Shifted Delta Cepstral Features

    Pedro A. Torres-Carrasquillo;Pedro A. Torres-Carrasquillo;Elliot Singer;Mary A. Kohler;Richard J. Greene

  • Sheep, Goats, Lambs and Wolves: A Statistical Analysis of Speaker Performance in the NIST 1998 Speaker Recognition Evaluation

    George R. Doddington;Walter Liggett;Alvin F. Martin;Mark A. Przybocki

  • Language Recognition via i-vectors and Dimensionality Reduction.

    Najim Dehak;Pedro A. Torres-Carrasquillo;Douglas A. Reynolds;Réda Dehak

  • Comparison of background normalization methods for text-independent speaker verification.

    Douglas A. Reynolds

  • Deep Neural Network Approaches to Speaker and Language Recognition

    Fred Richardson;Douglas Reynolds;Najim Dehak

  • Experimental evaluation of features for robust speaker identification

    D.A. Reynolds

  • The NIST speaker recognition evaluation - overview methodology, systems, results, perspective

    Douglas A. Reynolds;George R. Doddington;George R. Doddington;Mark A. Przybocki;Alvin F. Martin

  • A Gaussian mixture modeling approach to text-independent speaker identification

    Douglas A. Reynolds

  • Modeling of the glottal flow derivative waveform with application to speaker identification

    M.D. Plumpe;T.F. Quatieri;D.A. Reynolds

  • Language Recognition via Ivectors and Dimensionality Reduction

    Najim Dehak;Pedro A. Torres-Carrasquillo;Douglas Reynolds;Reda Dehak

Frequent Co-Authors

William M. Campbell
William M. Campbell Amazon (United States)
Najim Dehak
Najim Dehak Johns Hopkins University
Alan V. McCree
Alan V. McCree Johns Hopkins University
George R. Doddington
George R. Doddington Texas Instruments (United States)
Tomi Kinnunen
Tomi Kinnunen University of Eastern Finland
Daniel Garcia-Romero
Daniel Garcia-Romero Johns Hopkins University
Junichi Yamagishi
Junichi Yamagishi National Institute of Informatics

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring Computer Science in the USA opens many doors to related online degrees and careers. For those seeking diverse opportunities, fields like online electrical engineering offer strong online electrical engineering career outcomes, including roles in software development and systems analysis.

Not everyone wants a lengthy degree. Many students and professionals opt for short certificate programs that pay well, gaining entry into tech roles such as IT support, data analysis, or cybersecurity with just a few months of study.

For those who want to accelerate their learning, it’s possible to earn one of the quickest masters degree online. These programs provide advanced knowledge in a short time, helping graduates qualify for specialized and higher-paying positions.

If career growth and salary are your primary goals, consider pursuing one of the most valuable masters degrees in tech. These in-demand programs are highly respected by employers and can significantly boost your career trajectory.

Best Scientists Citing Douglas A. Reynolds

Trending Scientists

Recently Published Articles