Use of a reliability coefficient in noise cancelling by neural net and weighted matching algorithms

The problems of e cacy estimation in noise cancelling by a neural net (LIN-Lateral Inhibition Net [5]) and the use of this information in weighting matching algorithms are focused. Since the e ect of noise on the speech signal is variable and the backpropagation training algorithm is essentially stochastic (most common patterns have more in uence in the weights re-estimation process), it is reasonable to suppose that the LIN e cacy depends on the input and each noisy frame could be associated to a reliability coe cient that attempts 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.