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Current methods for video description are based on encoder-decoder sentence generation using recurrent neural networks (RNNs). Recent work has demonstrated the advantages of integrating temporal attention mechanisms into these models, in which the decoder network predicts each word in the description by selectively giving more weight to encoded features from specific time frames. Such methods typically...
Recently, a novel speaker adaptation method was proposed that applied the Speaker Adaptive Training (SAT) concept to a speech recognizer consisting of a Deep Neural Network (DNN) and a Hidden Markov Model (HMM), and its utility was demonstrated. This method implements the SAT scheme by allocating one Speaker Dependent (SD) module for each training speaker to one of the intermediate layers of the front-end...
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