The problems of ecacy estimation in noise cancelling by a neural net ) and the use of this information in weighting matching algorithms are focused. Since the eect of noise on the speech signal is variable and the backpropagation training algorithm is essentially stochastic (most common patterns have more inuence in the weights re-estimation process), it is reasonable to suppose that the LIN ecacy depends on the input and each noisy frame could be associated to a reliability coecient that

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... ts to measure how reliable is the result of the neural net processing. Isolated word recognition experiments have shown that reliability weighting can result in a mean error rate reduction as high as 96, 80, 58 and 36 % at SNR=12, 6, 3 and 0dB, respectively, when the noise is white Gaussian.