Current speech recognition technology is based on mathematical-statistical models of language. Although these models have become extremely refined over the last de¬cades, progress in automated speech recognition has become very slow. Human-level speech recognition seems unreachable. The ORGANIC project ventures on an alto¬gether different route toward automated speech recognition: not starting from statistical models of language, but from models of biological neural information processing – from neurodynamical models.
ORGANIC will combine a large variety of neurodynamical mechanisms – of signal filtering, learning, short- and long-term memory, dynamical pattern recognition – into a complex neurodynamical "Engine" for speech recognition. A major scientific challenge of ORGANIC lies in the very complexity of the targeted architectures. In order to master the resulting nonlinear dynamical complexity, a special emphasis is put on mechanisms of adaptation, self-stabilization and self-organization. The overall approach is guided by the paradigm of Reservoir Computing, a biologically inspired perspective on how arbitrary computations can be learnt and performed in complex artificial neural networks.

R&D activities in ORGANIC will result in

  • a much deeper theoretical understanding of how very complex computations, especially those related to language processing, can be robustly and adaptively per¬formed in neurodynamical systems,
  • a publicly available Engine of programming tools which conforms to recent interface standards for parallel neural system simulations,
  • prototype implementations of large-vocabulary speech recognizers and handwriting recognition solutions.