基于对比预测编码模型的多任务学习语种识别方法

The key of language identification is to extract useful features from speech fragments. The time‑delayed neural network (TDNN) can extract feature vectors, which contain rich context and improve system performance effectively. This paper proposes a multi‑task learning method of ECAPA(Emphasized channel attention)‑TDNN+contrastive predictive coding(CPC) network for language identification. ECAPA‑TDNN is the main network to extract the global features of language. The improved CPC model is the auxiliary network, and the frame level features extracted by ECAPA‑TDNN are compared and predicted. Finally, the joint loss function is used to optimize the network. The proposed method is tested on the 10 language data sets provided by the AP17‑OLR data set. The result shows that the identification accuracy of the proposed network is higher than baseline on the 1 s, 3 s and All test data sets of AP17‑OLR.