Unsupervised neural networks for speech perception with Cochlear Implant systems for the profoundly deaf

Recently we have proposed a new speech processing concept for Cochlear Implant (CI)-systems, The concept is based on speaker independent signal representation and a neural net classifier which can be combined with the well known CI-speech-coding-strategies, This paper describes some new simulation results: For every speech input frame a 4-dimensional feature vector has been extracted by employing a relative spectral perceptual linear predictive (RASTA-PLP) technique. To classify the feature vectors into so called ''auditory related units (ARU)'' we applied the self-organizing Kohonen neural net The best matching ARU's will directly control the synthesis of a ''alphabet'' of patient adapted stimulus patterns, Simulation results show that the Kohonen algorithm finds representative clusters in the feature vector space for different net dimensions, A discussion of the results and a overview of present experiments with deaf patients will be given.