Optimized deep network based spoof detection in automatic speaker verification system

Neelima, M; Prabha, I. Santi

doi:10.1007/s11042-023-16127-w

Cited by 3 publications

(1 citation statement)

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“…ME3: A fault detection scheme employing a multilayer perceptron, representing a standard in neural network applications [ 23 ]. ME4: A scheme that applies signal forecasting methods, offering a perspective on the utility of time-series predictive analysis in fault detection [ 24 ]. ME5: A support vector machine-based fault detection scheme, capitalizing on the strengths of SVMs in pattern recognition [ 25 ].…”

Section: Case Studymentioning

confidence: 99%

Intelligent fault detection strategy for knowledge entities in fault semantic networks of distribution network based on siamese networks

Sun,

Qin,

Tong

et al. 2024

PLoS ONE

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The advent of smart grid technologies has brought about a paradigm shift in the management and operation of distribution networks, allowing for intricate system information to be encapsulated within semantic network models. These models, while robust, are not immune to faults within their knowledge entities, which can arise from a myriad of issues, potentially leading to verification failures and operational disruptions. Addressing this critical vulnerability, our research delves into the development of a novel fault detection methodology specifically tailored for the knowledge entity variables of semantic networks in distribution networks. In our approach, we first construct a state space equation that models the behavior of knowledge entity variables in the presence of faults. This foundational framework enables us to apply an unknown input observer strategy to effectively detect anomalies within the system. To bolster the fault identification process, we introduce the innovative use of a siamese network, a neural network architecture which is proficient in differentiating between similar datasets. Through simulation scenarios, we demonstrate the efficacy of our proposed fault detection method.

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Section: Case Studymentioning

confidence: 99%