Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Engineering
Application Of Deep Neural Networks To Distribution System State Estimation And Forecasting, James P. Carmichael, Yuan Liao
Application Of Deep Neural Networks To Distribution System State Estimation And Forecasting, James P. Carmichael, Yuan Liao
Electrical and Computer Engineering Faculty Publications
Classical neural networks such as feedforward multi-layer perceptron models (MLPs) are well established as universal approximators and as such, show promise in applications such as static state estimation in power transmission systems. The dynamic nature of distributed generation (i.e. solar and wind), vehicle to grid technology (V2G) and false data injection attacks (FDIAs), may pose significant challenges to the application of classical MLPs to state estimation (SE) and state forecasting (SF) in power distribution systems. This paper investigates the application of conventional neural networks (MLPs) and deep learning based models such as convolutional neural networks (CNNs) and long-short term networks …
Introducing Phonetic Information To Speaker Embedding For Speaker Verification, Yi Liu, Liang He, Michael T. Johnson
Introducing Phonetic Information To Speaker Embedding For Speaker Verification, Yi Liu, Liang He, Michael T. Johnson
Electrical and Computer Engineering Faculty Publications
Phonetic information is one of the most essential components of a speech signal, playing an important role for many speech processing tasks. However, it is difficult to integrate phonetic information into speaker verification systems since it occurs primarily at the frame level while speaker characteristics typically reside at the segment level. In deep neural network-based speaker verification, existing methods only apply phonetic information to the frame-wise trained speaker embeddings. To improve this weakness, this paper proposes phonetic adaptation and hybrid multi-task learning and further combines these into c-vector and simplified c-vector architectures. Experiments on National Institute of Standards and Technology …