Projects

This project deals broadly with problems in the analysis of the way in which quantificational material is integrated with non-quantificational material in noun phrases in natural language. It studies these interactions with particular reference to the behavior of the comparative and superlative and quantificational adjectives (‘much/little’), and with particular reference to Arabic, which has cross-linguistically rare properties that shed new light on the these issues.

The primary objective is to develop an Explainable Artificial Intelligence (XAI) system dedicated to dialect classification.

Intelligent analysis of Erdprofi/Erdmobile data

The project explores the use of AI and advanced data science methods for the in-depth analysis of telco services automated test results.

In a series of co-operation projects with Lego, OFAI has developed technology which allows generating verbal building instructions from abstract representations based on which Lego's visual building instructions are generated. With these verbal instructions that can be accessed via speech or Braille, blind and seeing impaired are being enabled to construct and experience LEGO models on their own.

The project addresses questions of what it means to develop good and trustworthy AI applications. What are the ethical, legal and technical constraints to take into account and how to develop concrete ethics-by-design principles and implement these in a technical platform aiming for truly explainable and accountable AI? The research project brings together experts from ethics, AI ethics, computer science, AI, natural language processing, and data science.

An extended investigation of the (in)stability of phonemic quantity in vowel plus consonant (VC) sequences in southern German varieties has provided new evidence (1) for the Bavarian VC timing system to be currently changing both in Austria and Germany primarily due to dialect levelling, (2) for the cross-generational stability of VC patterns in Swiss dialects, and (3) for the emergence of aspirated stops in younger speakers of Bavarian and Swiss dialects.

DANCR is an artistic, AI-driven tool. Improvisation is of utter importance for contemporary dance. DANCR uses AI models built on recordings of dance improvisations where a Pepper robot imitates the artist to build models for new and creative human-robot interactions during new live dance szenarios.

The project researches human-robot interaction from a collaborative point of view where the mutual ability of human and robot to understand each other’s signals is core to the successful completion of joint tasks, and also plays a major role for human acceptance of and trust in the robot as a workmate. We study close collaborations of humans with cobots employing both Virtual Reality scenarios and real world encounters with a collaborative robot in industry-relevant scenarios.

The main target of STADIEM/Aiconix is to foster language technology for dialects and regional varieties. Using the AI-based platform provided by Aiconix, clients will be able to access and interact with huge amounts of digital media content, whereas the principal aim of this project is to significantly enhance ASR with respect to speech data from regional standards and dialects. In this 'develop phase' we primarily focus on Austrian varieties while foreseeing to scale up the methods upon other varieties of German and other languages.