An effective method for redundant features and long tail effect in accent recognition
Accent detection refers to the process of identifying different regional accents within the same language class.To enhance the accuracy of accent detection,we employed several methods and then the obvious effect was obtained.Firstly,in order to solve the problem that accent detection features do not highlight the weight of key features,the attention mechanism is introduced,and a variety of attention mechanisms are compared and analyzed.The performance of accent detection is improved through the model adaptive learning channel and different weights of spatial dimensions.The experiment results on the English accent datasets named Common Voice show that the introduction of CBAM attention module is effective,with a relative improvement of 12.7％in accuracy and 17.9％in precision and 6.98％in F1-score parameters.After that,we proposed a Tree-Form based classification method to alleviate the long-tail effect,and the accuracy parameter is improved by 5.2％at most.Inspired by domain adversarial training(DAT),we attempted to eliminate redundant information of accent via adversarial training.The relative improvement of accuracy parameter is up to 3.4％,and the relative improvement of recall parameter is up to 16.9％.
万方数据
维普
