The signal change-point detection using the high-order statistics of log-likelihood difference functions

In this paper, a supervised neural network based signal change-point detector is proposed. The proposed detector uses some high order statistics of log-likelihood difference functions as the input features in order to improve the detection performance. These high order statistics can be easily calculated from the CCGMM coefficients of signals. Performance of the proposed signal change-point detector was examined by using a database of five-hour TV broadcast news. Experimental results showed that the equal error rate (EER) was improved from 16.6% achieved by the baseline method using the CCGMM-based divergence measure to 14.4% by the proposed method.