LEVERAGING NATIVE LANGUAGE SPEECH FOR ACCENT IDENTIFICATION USING DEEP SIAMESE NETWORKS
Table 7: N-best accuracies (%) on the test set from both individual systems and combined systems.
['System', '1-best', '2-best', '3-best']
We observe that the accuracies dramatically improve across all three individual systems when moving from 1-best to 2-best and 3-best accuracies. This is indicates that a significant part of the confusions seen across the classes are confined to the top 3 predictions. We also combine the outputs from the three individual systems. We adopt a simple majority voting strategy: Choose the prediction that 2 or more systems agree upon. If all three systems predict different accent classes, then choose the accent predicted by Siamese-4. We also use a weighted voting strategy. The outputs from Siamese-4 were converted into posterior probabilities using a softmax layer. A weighted combination of the probabilities from SVM, NNET and Siamese-4 was used to determine the accent with the highest probability. (Weights of 0.3, 0.3 and 0.4 were used for SVM, NNET and Siamese-4, respectively.) As expected, we observe performance gains from combining the outputs of all three individual systems; the gains are larger in the 2-best and 3-best cases.