Mitchell McLaren

Senior Computer Scientist, Speech Technology and Research Laboratory (STAR)

Mitchell McLaren, Ph.D., is an advanced research engineer in SRI International’s Speech Technology and Research (STAR) Laboratory. His research interests include speaker and language identification, as well as other biometrics such as face recognition.

At SRI, McLaren is task lead of the Speaker Identification task for the Defense Advanced Research Projects Agency (DARPA) Robust Automatic Transcription of Speech (RATS) program. In past projects, he was heavily involved in the Threshold Calibration for Speaker Identification project from the Federal Bureau of Investigation and in the production of SRI’s submission to the 2012 Speaker Recognition Evaluation held by the National Institute of Standards and Technology.

Prior to joining SRI, McLaren was a postdoctoral researcher and the University of Nijmegen, The Netherlands where he focused on speaker and face identification on the Bayesian Biometrics for Forensics (BBfor2) project, funded by the Marie Curie Actions Research Fellowship Programme.

McLaren has published more than 20 papers in the field of speaker, language and face recognition. View selected publications on Google Scholar.

His Ph.D. in speaker identification is from the Queensland University of Technology (QUT), Brisbane, Australia.

View Dr. McLaren’s LinkedIn profile.

Recent publications

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Toward Fail-Safe Speaker Recognition: Trial-Based Calibration with a Reject Option (1/1/2019) - The output scores of most speaker recognition systems are not directly interpretable as stand-alone values. For this reason, a calibration…

Robust Speaker Recognition from Distant Speech under Real Reverberant Environments Using Speaker Embeddings (9/1/2018) - This article focuses on speaker recognition using speech acquired using a single distant or far-field microphone in an indoors environment.…

Analysis of Complementary Information Sources in the Speaker Embeddings Framework (9/1/2018) - Deep neural network (DNN)-based speaker embeddings have resulted in new, state-of-the-art text-independent speaker recognition technology. However, very limited effort has…