Audiovisual media content is an essential resource of modern history. It’s our way to communicate and to entertain. To fully benefit from multilingual audiovisual content, we need efficient tools to make visual content accessible in words.
We provide new methods that help to translate moving images and sounds into words. MeMAD methods will help us to manage large amounts of audiovisual data cost efficiently.
Anyone using audiovisual content will benefit from MeMAD. Professionals who work in the Creative Industries will receive new methods for video management and digital storytelling. Visually and hearing impaired, among others, will have better access to video content.
MeMAD is an EU funded H2020 research project (2018-2020). MeMAD will develop methods for an efficient re-use and re-purpose of multilingual audiovisual content targeting to revolutionize video management and digital storytelling in broadcasting and media production.
“We go far beyond the state-of-the-art automatic video description methods by making the machine learn from the human. The resulting description is thus not only a time-aligned semantic extraction of objects but makes use of the audio and recognizes action sequences.”
MeMAD’s main results are detailed, rich descriptions of the moving images, speech, and audio. We integrate the latest research achievements in deep neural network techniques in computer vision with knowledge bases, human and machine translation in a continuously improving machine learning framework.
MeMAD project partners have received recognition for their accomplishments. Three project innovations were recognized by the European Commission’s Innovation Radar. Innovation Radar is a European Commission initiative that champions potential…
One of MeMAD’s objectives has been to produce fully automated subtitles that are created from source language speech with automatic speech recognition, automatic segmentation and machine translation. We have previously conducted…
At the end of July, MeMAD featured in a volume of ground-breaking research in the field of multimedia accessibility with the publication of Innovation in Audio Description Research (Routledge), an…
Maaseudun Tulevaisuus published two articles about machine translation and the MeMAD project, interviewing our researcher Maarit Koponen from the University of Helsinki Read more (in Finnish): Machine can translate and…
MeMAD translation models made in Helsinki are now integrated in Hugging Face transformers, the world’s most popular library for the workwith deep neural NLP models. They can be used by…
Imagine that a tourist calls an emergency service speaking a foreign language. How to find a person that speaks the right language? Or you have tons of multilingual television broadcasts…
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