Three topics development (Q2)

The first project I have chosen to explain is the VSDS: Viennese Sociolect and Dialect Synthesis which is being developed by Fiedrich Neubarth, who belong to the OFAI Language Technology Group. One important means of natural human-computer interaction is spoken language, so for a variety of applications it is essential to have high quality speech synthesis for different languages. The outcome of this project will be high quality synthetic voices, which allow a computer to “speak” in different Viennese dialects/sociolects. Since the sources of these voices are pieces taken from actual human speech, the outcome of the synthetic voices will sound very natural, close to human speech. With this technology it is possible to realize a lot of applications from the domain of education and tourism to art. A mobile sample application, a Viennese district guide capable of various dialects or variants, is also developed within the project. In the research part of the project efficient methods are investigated for developing synthetic voices for languages that are variants of other languages. Furthermore, it is necessary to employ methods for switching, or shifting between the standard language and dialectal variants, which reflects the fact that this mixing of standards corresponds to the everyday language use of many speakers. User tests are conducted to evaluate the quality of the synthetic voices and of the relevant sample applications.

The second research project explained is from the Edimburgh Language Technology Group. Ewan Klein, Claire Grover as principal investigators from the University of Edimburgh and Chris Manning from Standford University have developed EASIE, which builds on existing techniques for information extraction (IE) in order to develop and implement improved methods for extracting semantic content from text. The results of the research are being used to significantly extend the functionality of Edinburgh’s existing XML-based LT-TTT software, in part by incorporating machine learning approaches developed at Stanford. The objective is to develop and implement improved methods for extracting semantic content from text.

The last project which I will focus on is K-Space, developed by Thierry Declerck, from the Language Technology Lab. It is a network of leading research teams from academia and industry conducting integrative research and dissemination activities in semantic inference for automatic and semi-automatic annotation and retrieval of multimedia content. The aim of K-Space research is to narrow the gap between low-level content descriptions that can be computed automatically by a machine and the richness and subjectivity of semantics in high-level human interpretations of audiovisual media: The Semantic Gap. The Network of Excellence K-Space exploits the complementary expertise of project partners, enables resource optimization and fosters innovative research in the field. Specifically, K-Space integrative research focus on three areas:

  • Content-based multimedia analysis:
    Tools and methodologies for low-level signal processing, object segmentation, audio/speech processing and text analysis, and audiovisual content structuring and description.
  • Knowledge extraction:
    Building of a multimedia ontology infrastructure, knowledge acquisition from multimedia content, knowledge-assisted multimedia analysis, context based multimedia mining and intelligent exploitation of user relevance feedback.
  • Semantic multimedia:
    knowledge representation for multimedia, distributed semantic management of multimedia data, semantics-based interaction with multimedia and multimodal media analysis.

Sources:

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