Interspeech 2020 An Adaptive X Vector Model For Text Independent Speaker Verification

Interspeech 2020 An Adaptive X Vector Model For Text In Experiments are carried out on the speaker in the wild (sitw) and voices databases. the results demonstrate that the proposed methods significantly outperform the original x vector approach. doi: 10.21437 interspeech.2020 1071 cite as: gu, b., guo, w., ding, f., ling, z. h., du, j. (2020) an adaptive x vector model for text independent speaker. An adaptive x vector model for text independent speaker verification bin gu, wu guo, fenglin ding, zhenhua ling, jun du national engineering laboratory for speech and language information processing, university of science and technology of china, hefei, china {bin2801,flding}@mail.ustc.edu.cn, {guowu,zhling,jundu}@ustc.edu.cn abstract.

Figure 2 From An Adaptive X Vector Model For Text Independent ођ An adaptive x vector model for text independent speaker verification bin gu, wu guo, fenglin ding, zhen hua ling, jun du shouted speech compensation for speaker verification robust to vocal effort conditions. Abstract: in this paper, adaptive mechanisms are applied in deep neural network (dnn) training for x vector based text independent speaker verification. first, adaptive convolutional neural networks (acnns) are employed in frame level embedding layers, where the parameters of the convolution filters are adjusted based on the input features. In this paper, adaptive mechanisms are applied in deep neural network (dnn) training for x vector based text independent speaker verification. first, adaptive convolutional neural networks (acnns) are employed in frame level embedding layers, where the parameters of the convolution filters are adjusted based on the input features. compared with conventional cnns, acnns have more flexibility in. Request pdf | on oct 25, 2020, bin gu and others published an adaptive x vector model for text independent speaker verification | find, read and cite all the research you need on researchgate.

An Adaptive X Vector Model For Text Independent Speaker ођ In this paper, adaptive mechanisms are applied in deep neural network (dnn) training for x vector based text independent speaker verification. first, adaptive convolutional neural networks (acnns) are employed in frame level embedding layers, where the parameters of the convolution filters are adjusted based on the input features. compared with conventional cnns, acnns have more flexibility in. Request pdf | on oct 25, 2020, bin gu and others published an adaptive x vector model for text independent speaker verification | find, read and cite all the research you need on researchgate. An adaptive x vector model for text independent speaker verification bin gu, wu guo, lirong dai, jun du national engineering laboratory for speech and language information processing, university of science and technology of china, hefei, china [email protected],{guowu,lrdai,jundu}@ustc.edu.cn abstract. An adaptive x vector model for text independent speaker verification.

Table 2 From An Adaptive X Vector Model For Text Independent An adaptive x vector model for text independent speaker verification bin gu, wu guo, lirong dai, jun du national engineering laboratory for speech and language information processing, university of science and technology of china, hefei, china [email protected],{guowu,lrdai,jundu}@ustc.edu.cn abstract. An adaptive x vector model for text independent speaker verification.

Interspeech 2020 Adaptive Neural Speech Enhancement With A Denoising