Target sample mining with modified activation residual network for speaker verification.
In the domain of speaker verification, Softmax can be used as a backend for multi-classification, but traditional Softmax methods have some limitations that limit performance. During the training phase, Softmax is used for multi-class training, while the speaker verification stage is a binary classi...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0320256 |
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| Summary: | In the domain of speaker verification, Softmax can be used as a backend for multi-classification, but traditional Softmax methods have some limitations that limit performance. During the training phase, Softmax is used for multi-class training, while the speaker verification stage is a binary classification validation, leading to a discrepancy between the multi-class training in the training phase and the binary classification validation in the verification stage. It is also important to notice the issue of the disparity in the number of positive and negative samples in the sampling process of a binary classification problem. The difference in positive and negative samples can lead to the dominance of negative sample gradients during machine learning training, which can affect the performance of the speaker verification system. During the process of calculating similarity between positive and negative samples, there may be encountered an issue of overlapping similarity scores. If the overlapping portion is too large, it can reduce the discriminability between positive and negative samples, affecting the speaker system's ability to distinguish between positive and negative samples. Considering the relatively compact distribution of positive and negative sample spaces, it is beneficial for enhancing the performance of the speaker system, and focusing more on the learning of difficult samples is conducive to improving the network's convergence and generalization. Thus, this paper introduces an adaptive target function capable of solving these issues (SphereSpeaker). SphereSpeaker introduces different types of hyperparameters on the basis of Softmax, making it more suitable for handling speaker verification problems. SphereSpeaker also introduces three different angular margins to update the network, further enhancing the stability and generalization ability of the network model. Meanwhile, considering the issues of gradient vanishing, gradient explosion, and model degradation that can occur in deep neural networks, this paper introduces a deep neural network, which is named as Residual Network PReLu(ResNet-P). The experimental results indicate that compared to other deep neural network methods, this method has the lowest equal error rate, significantly improving the performance of the speaker verification system. |
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| ISSN: | 1932-6203 |