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Unified Transcription and Translation for Extended Reality

Project description

Transforming language translations and interpretations

Language models are used to build machine learning technologies for predicting the next word or character in a document. Researchers in the EU-funded UTTER project will use large language models to build the next generation of multimodal extended-reality technologies for transcription, translation, summarisation and minuting. The technologies will be optimised, making the models scalable, adaptable, contextualised, robust, explainable and emotion-aware. Moreover, use case prototypes in the project will focus on a personal assistant for meetings that can improve communication in the online world and an advanced customer service assistant to support global markets.

Objective

The aim of UTTER is to leverage large language models to build the next generation of multimodal eXtended reality (XR) technologies for transcription, translation, summarisation, and minuting. We will make these technologies scalable, adaptable, contextualised, robust, explainable, and emotion-aware. We will increase the context-sensitivity of the technologies, so they can take into account the full history of the conversation, as well as its wider context. We will introduce confidence-aware models, which can take into account their own limitations. We will develop explainable models, so the human user can know why the model made the decisions it did. We will improve adaptation, so that domain-specific and language-specific models can be quickly rolled out. For these advances we will make use of pre-trained eXtended reality (XR) models, which optimally combine text and speech signals, and are trained efficiently with adapters and prompting. We will also develop efficient methods to deploy such large and complex models, so that they can put into production in an energy-efficient manner. Our use-case prototypes will cover (i) A personal assistant for meetings that can improve communication in the online world and (ii) an advanced customer service assistant to support global markets. These prototypes will be developed and tested throughout the project, with annual releases and evaluations. Through our cascaded grant programme, and our release of tools to facilitate the use of pre-trained XR models, will enable the take-up and development of these technologies throughout Europe.

Fields of science (EuroSciVoc)

CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.

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Keywords

Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)

Programme(s)

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Topic(s)

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Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

HORIZON-RIA - HORIZON Research and Innovation Actions

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) HORIZON-CL4-2021-HUMAN-01

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Coordinator

UNIVERSITEIT VAN AMSTERDAM
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 1 998 946,89
Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 2 003 416,00

Participants (3)

Partners (1)

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