An End-to-End Macaque Voiceprint Verification Method Based on Channel Fusion Mechanism

Primates are facing a serious survival crisis. Tracking the range of animal activities and population changes is of great significance for efficient animal protection. Primates are highly alert and inaccessible to humans so that it is difficult to track animals through direct observation, DNA fingerprinting, or marking methods. Primate recognition based on animal calls has the advantages of wide monitoring range, low equipment cost, and good concealment. In this work, we propose an effective macaque speech feature extraction structure, and innovatively propose a feature fusion mechanism to effectively obtain the feature representation of each call. Furthermore, we construct a public open source macaque voiceprint verification dataset. The experimental results show that the proposed method is superior to the existing state-of-the-art human voiceprint verification algorithms with different call durations. The equal error rate (EER) of our macaque voiceprint verification algorithm reaches 6.19%.