A compact 1D-CNN trained on FFT coefficients and noise-augmented clips reaches 97.87% validation accuracy on a small, same-session custom speaker dataset.
A deep neural network model for speaker identification,
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Towards Speaker Identification with Minimal Dataset and Constrained Resources using 1D-Convolution Neural Network
A compact 1D-CNN trained on FFT coefficients and noise-augmented clips reaches 97.87% validation accuracy on a small, same-session custom speaker dataset.