Hybrid architecture based on fuzzy classifier and multiplayer feed-forward neural network for speaker identification - ZHANG Ling-hua; YANG Zhen; ZHENG Bao-yu (Tongxin xuebao)
A novel method for speaker identification was proposed which was based on a fuzzy classifier with hyperellipsoidal regions. First, the training data for each class were divided into several clusters. Then, for each cluster, a fuzzy rule with a hyperellipsoidal region was defined around a cluster center. The evaluation experiments had been conducted to compare the fuzzy hyperellipsoidal classifier with the HMM. It was found that the former classifier can achieve a comparable speaker identification performance to the latter with higher clustering speed. Further research showed that both fuzzy hyperellipsoidal classifier and the HMM worsened the recognition ability when the test data contained noise. To overcome this problem, a hybrid architecture based on fuzzy classifier and multilayer feed-forward neural network （MLFNN） was developed for speaker recognition. The experimental results showed that the new method can achieve a much better identification performance and robustness to the additive noise.