SOURCE-LINKED INTELLIGENCE

Unified Transcription and Translation for Extended Reality

CORDIS · observation · Publication date unknown

Unified Transcription and Translation for Extended Reality 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

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recordType

award

status

SIGNED

region

EU

value

4070321.89

unit

EUR