News: Home Highlights

HLTCOE Team Wins Best Paper Award at Odyssey 2020

December 3, 2020

A team of speech researchers from the HLTCOE won this year’s best paper award at the Odyssey 2020 conference, the premiere international academic / industry conference for speaker and language recognition. The team consisted of Dr. Daniel Garcia-Romero, Dr. Greg Sell, and Dr. Alan McCree. They were selected for this honor for their paper entitled […]

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Dr. David Hunter Joins the HLTCOE as a Visiting Fellow

October 22, 2020

The HLTCOE is pleased to announce that Dr. David Hunter, former chair of the Statistics Department at Pennsylvania State University, will join us this year as a visiting fellow. During his fellowship Dr. Hunter hopes to explore the statistical properties of deep learning models as they’re applied to speech and text processing problems by collaborating […]

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HLTCOE Team Takes First Place in Duolingo MT Competition

October 19, 2020

A team from JHU in May 2020 took first place in a machine translation (MT) competition sponsored by Duolingo, the world’s leading language-learning platform. Led by HLTCOE staff researcher Matt Post and JHU Ph.D. student Huda Khayrallah (CLSP), and spear-headed by COE intern Jake Bremerman (masters student at the University of Maryland, College Park), their […]

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Dr. Peter Viechnicki joins the HLTCOE as its new Director

September 15, 2020

Dr. Peter Viechnicki, a highly respected technologist with extensive experience as a research manager, has been appointed to serve as Director of the Human Language Technology Center of Excellence (HLTCOE).  Viechnicki, who joined the University on 15 September, received his PhD in Linguistics from the University of Chicago with a dissertation in phonetics. He brings […]

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HLTCOE a Top Performer at VoxSRC

January 29, 2020

JHU HLTCOE was a top performer in a recent open speaker recognition challenge called VoxSRC, finishing in the top two of more than fifty entries from the international research community. The challenge, hosted by the University of Oxford, was based on the open-source VoxCeleb speech corpus captured from public celebrity videos with automatic speaker labeling […]

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Processing videos by combining visual and audio cues

December 12, 2019

Videos include information in both the visual and audio domains, and so video processing techniques should utilize both of these means for more effective solutions.  The HLTCOE has been researching this strategy since 2017, when recognizing individuals in videos using both voice and face was a topic at the SCALE summer workshop.  The work resulting […]

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A Dataset to Support Research on Bilingual Lexicons for Machine Translation (MT)

November 25, 2019

Bilingual lexicons (or bilingual dictionaries) are valuable resources for machine translation. For example, when working with technical documents like patents, a bilingual lexicon consisting of technical jargon is important for ensuring that the translation is precise and correct. At the conference on Empirical Methods in Natural Language Processing (EMNLP) in November 2019, JHU researchers released […]

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Analyzing Neural Models by Freezing Subnetworks

October 15, 2018

by Kevin Duh, Senior Research Scientist   The SCALE2018 Machine Translation workshop focused on building resilient neural machine translation systems for new domains. In addition to developing new algorithms to improve translation accuracy, the team also dedicated significant efforts to analysis techniques in order to understand when and why neural networks work. Neural network models, […]

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JHU Team Gets High Marks in DIHARD Challenge

October 15, 2018

by Greg Sell, Senior Research Scientist   A team of Johns Hopkins researchers from the HLTCOE and CLSP participated in the recent DIHARD challenge. In the evaluation, teams were given audio recordings of speech from a diverse set of conditions with an unknown number of speakers, with the goal of correctly marking the times that each […]

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MT/IE: Cross-lingual Open Information Extraction with Neural Sequence-to-Sequence Models

February 24, 2017

Cross-lingual information extraction is the task of distilling facts from foreign language (e.g. Chinese text) into representations in another language that is preferred by the user (e.g. English tuples). Conventional pipeline solutions decompose the task as machine translation followed by information extraction (or vice versa). We propose a joint solution with a neural sequence model, […]

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Human Language Technology Center of Excellence