We propose a batch-wise maximum softmax loss, in which the non-target logits are replaced by the ones derived from the whole batch.
In recent years, deep neural networks have made significant progress in the speaker verification task [1,2,3]. To further advance the performance and ...
Jul 17, 2023 · [15] Li Ruida, Fang Shuo, Ma Chenguang, and Li Liang,. “Adaptive rectangle loss for speaker verification,” Proc. ISCA Interspeech, pp. 301 ...
Jun 15, 2021 · In this paper, we pro-pose a novel angular loss function called adaptive margin cir-cle loss for speaker verification.
Missing: Rectangle | Show results with:Rectangle
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Jun 15, 2021 · A novel angular loss function called adaptive margin cir-cle loss for speaker verification, which has flexi-ble optimization and definite ...
Sep 10, 2024 · The mismatch between close-set training and open-set testing usually leads to significant performance degradation for speaker verification ...
Oct 6, 2020 · The results show that the proposed method is effective for SV in the challenging conditions and performs better than the baseline i-vector and ...
Most of the current excellent models in speaker verification are ResNet-based deep models and attention-based models. These models have a general weakness, ...
A voice activity detector (VAD) plays a vital role in robust speaker verification, where energy VAD is most commonly used. Energy. VAD works well in noise-free ...
Jul 23, 2021 · A method of text-independent speaker recognition based on deep residual networks model was proposed in this paper.
Missing: Rectangle | Show results with:Rectangle