Co-talker Separation Using the 'Cocktail Party Effect'
An artificial neural network (ANN) speech-classifier-controlled iterative filtering system is described, which simulates the cocktail party effect for speech separation. The ANN speech classifier controls a modified iterative Wiener filter to cancel the interference by setting the filter's parameter's and the convergence criterion for the iteration. The proposed system has been employed successfully with multiple-microphone speech acquisition systems for co-talker speech separation. The simulation results have shown that the iterative processing controlled by the neural network consistently provides speech of good quality and intelligibility.
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