ASVtorch toolkit: Speaker verification with deep neural networks
نویسندگان
چکیده
منابع مشابه
Improving Deep Neural Networks Based Speaker Verification Using Unlabeled Data
Recently, deep neural networks (DNNs) trained to predict senones have been incorporated into the conventional i-vector based speaker verification systems to provide soft frame alignments and show promising results. However, the data mismatch problem may degrade the performance since the DNN requires transcribed data (out-domain data) while the data sets (indomain data) used for i-vector trainin...
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Kinship verification from facial images is an interesting and challenging problem. The current algorithms on this topic typically represent faces with multiple low-level features, followed by a shallow learning model. However, these general manual features cannot well discover information implied in facial images for kinship verification, and thus even current best algorithms are not satisfying...
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In this paper, a novel Convolutional Neural Network architecture has been developed for speaker verification in order to simultaneously capture and discard speaker and non-speaker information, respectively. In training phase, the network is trained to distinguish between different speaker identities for creating the background model. One of the crucial parts is to create the speaker models. Mos...
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To improve speaker verification performance, we extend the wellknown Probabilistic Neural Networks (PNN) to Locally Recurrent Probabilistic Neural Networks (LRPNN). In contrast to PNNs that possess no feedbacks, LRPNNs incorporate internal connections to the past outputs of all recurrent neurons, which render them sensitive to the context in which events occur. Thus, LRPNNs are capable of ident...
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We recently proposed the use of deep neural networks (DNN) in place of Gaussian Mixture models (GMM) in the i-vector extraction process for speaker recognition. We have shown significant accuracy improvements on the 2012 NIST speaker recognition evaluation (SRE) telephone conditions. This paper explores how this framework can be effectively used on the microphone speech conditions of the 2012 N...
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ژورنال
عنوان ژورنال: SoftwareX
سال: 2021
ISSN: 2352-7110
DOI: 10.1016/j.softx.2021.100697