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Multimodal Information Group

We research and develop measurement and evaluation methods to advance and promote the use of technologies that provide more effective access to multimedia and multi-lingual information.

The group focuses on measurement and evaluation methods to facilitate the use of technologies that provide easier access to multimedia and multi-lingual information and that improve human-computer interface modalities. These technologies include recognizing and/or transforming information in speech, text, images, video, and other multimedia modalities, and the fusion of heterogeneous media content.  Focus areas include speech recognition, speaker recognition, language recognition, machine translation, image processing, image understanding, video processing, visual recognition, 2-D and 3D shape analysis, image quality assessment, and interoperable digital media access.

Projects and Programs

Data Science

Ongoing
The NIST Information Access Division (IAD) initiated a Data Science Research Program (DSRP) aimed to advance the measurement science for big data and data

Interdisciplinary Projects

Ongoing
Some of the Multimodal Information Group's project areas span across multiple research areas within the group or to other groups in IAD. These interdisciplinary

Machine Translation

Ongoing
The Multimodal Information Group's machine translation (MT) program includes several activities contributing to machine translation technology and metrology

Speaker and Language Recognition

Ongoing
Our Speaker and Language Recognition program includes several activities contributing to speaker and language recognition technology and metrology advancements

Publications

Voice Biometrics: Future Trends and ChallengesAhead

Author(s)
Doug Reynolds, Craig Greenberg
Voice has become woven into the fabric of everyday human-computer interactions via ubiquitous assistants like Siri, Alexa, Google, Bixby, Viv, etc. The use of

Open Media Forensics Challenge (OpenMFC) 2020-2021: Past, Present, and Future

Author(s)
Haiying Guan, Yooyoung Lee, Lukas Diduch, Jesse Zhang, Ilia Ghorbanian Bajgiran, Timothee Kheyrkhah, Peter Fontana, Jonathan G. Fiscus
This document describes the online leaderboard public evaluation program, Open Media Forensics Challenge (OpenMFC) 2021-2022. In the report, first, the

Contacts