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ENR1 Multiple signal representations as a means to increase robustness against adverse conditions

Perception research suggests that speech processing involves competition between multiple, intermediate representations. In realistic conditions these can contain conflicting information.

Goal: Rather than decoding a single spectro-temporal speech representation in a fixed manner into ever larger units (sub-phone units, phones, words), we aim to develop a computational model that can use multiple (e.g., articulatory, short-term and long-term spectro-temporal) signal representations for automatically learning and exploiting the redundancy of the speech.

Relevance: Comparing recognition errors of the model with those of humans for speech that differs in degree of spontaneity and environmental noise (or for synthetic stimuli) will provide valuable insights in how humans deal with variations induced by realistic adverse conditions.

Main host institution: Radboud University Nijmegen

Second host institution: Technical University of Denmark

Industry partner: Nuance Communication International bvba

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INSPIRE News

  • We would like to warmly invite you to join our Radboud Summer School course on "Multilingualism in the Wild" Radboud University Nijmegen, The Netherlands http://www.ru.nl/radboudsummerschool/ Dates: 10-14...

  • The INSPIRE workshop "Computational models of cognitive processes" will take place in Leuven, Belgium, from Wednesday 1 July to Saturday 4 July, 2015. Click here for workshop...

  • The INSPIRE winter school "Talker-listener interactions" will take place in London, England, from Tuesday 20 January to Friday 23 January, 2015. Click here for winter school information.

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