Abstract
This paper presents an integrated neural network for voiceprint verification. The system implements two types of deep architecture: ResNet-18 and SincNet to extract the acoustic features. The triplet loss function is used to distinguish same-speaker and different-speaker pairs based on cosine similarity during the training phase. Experiments on three different datasets reveal that integrated system exceeds the baseline of DNN-based i-vector. The system reduces equal error rates (EERs) from the baseline method by 59.7, 54.5 and 58% on the datasets—Voxceleb1, LibriSpeech and AISHELL-1, respectively. In addition, the integrated model decreases the EERs in the single models.
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Acknowledgment
The project is supported by Sichuan Science and Technology Program (2018GZDZX0038).
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Jun, W., Yu, Z., Wenhao, S. (2021). A Novel Voiceprint Verification Technology Through Deep Neural Network. In: WU, C.H., PATNAIK, S., POPENTIU VLÃDICESCU, F., NAKAMATSU, K. (eds) Recent Developments in Intelligent Computing, Communication and Devices. ICCD 2019. Advances in Intelligent Systems and Computing, vol 1185. Springer, Singapore. https://doi.org/10.1007/978-981-15-5887-0_55
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DOI: https://doi.org/10.1007/978-981-15-5887-0_55
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