In this paper, we study a countermeasure module to detect spoofing attacks with converted or synthesized speech in tandem automatic speaker verification (ASV). Our approach integrates representation learning and transfer learning methods. For representation learning, good embedding network functions are learned from audio signals with the goal to distinguish different types of spoofing attacks. For transfer learning, the embedding network functions are used to initialize fine-tuning networks. We experiment well-known neural network architectures and front-end raw features to diversify and strengthen the information source for embedding. We participate in the 2019 Automatic Speaker Verification Spoofing and Countermeasures Challenge (ASVspoof 2019) and evaluate the proposed methods with the logical access condition tasks for detecting converted speech and synthesized speech. On the ASVspoof 2019 development set, our best single system achieves a minimum tandem decision cost function of nearly 0 during system development. On the ASVspoof 2019 evaluation set, our primary system achieves a minimum tandem decision cost of 0.1791, and an equal error rate (EER) of 9.08%. Our system does not have over-training issue as it achieves decent performance with unseen test data of the types presented in training, yet the generalization gap is not small with mismatched test data types.

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Based on the land use data of 2000, 2010 and 2020, using GIS technology along with landscape ecology methods, this paper monitored the changes in land use and landscape pattern in Wuhan. The findings are as follows: (1) the main features of land use change in Wuhan were the expansion of urban area and the decline of cropland, forest, wetland and water in recent 20 years; (2) forest, wetland and water kept a transfer-out trend while urban kept a transfer-in trend; (3) the fragmentation degree of forest, grassland and urban landscapes decreased from 2000 to 2020; (4) the patch shapes of almost all landscapes tended to be more regular under the human interventions. It is thereby worth reducing the interference intensity of human activities on landscape pattern in the process of urban growth.