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Abstract

This study aims is to establish a small system of text-independent recognition of speakers for arelatively small group of speakers at a sound stage. The fascinating justification for the International Space Station(ISS) to detect if the astronauts are speaking at a specific time has influenced the difficulty. In this work, we employedMachine Learning Applications. Accordingly, we used the Direct Deep Neural Network (DNN)-based approach, inwhich the posterior opportunities of the output layer are utilized to determine the speaker’s presence. In line withthe small footprint design objective, a simple DNN model with only sufficient hidden units or sufficient hiddenunits per layer was designed, thereby reducing the cost of parameters through intentional preparation to avoid thenormal overfitting problem and optimize the algorithmic aspects, such as context-based training, activation functions,validation, and learning rate. Two commercially available databases, namely, TIMIT clean speech and HTIMIT multi-handset communication database and TIMIT noise-added data framework, were tested for this reference model thatwe developed using four sound categories at three distinct signal-to-noise ratios. Briefly, we used a dynamic pruningmethod in which the conditions of all layers are simultaneously pruned, and the pruning mechanism is reassigned.The usefulness of this approach was evaluated on all the above contact databases

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