Abstract
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.
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References
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© 1995 Springer-Verlag Berlin Heidelberg
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Leisenberg, M. (1995). Unsupervised neural networks for speech perception with Cochlear Implant systems for the profoundly deaf. In: Mira, J., Sandoval, F. (eds) From Natural to Artificial Neural Computation. IWANN 1995. Lecture Notes in Computer Science, vol 930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-59497-3_210
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DOI: https://doi.org/10.1007/3-540-59497-3_210
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