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Table 1 Dataset descriptions

From: Deep neural network-based bottleneck feature and denoising autoencoder-based dereverberation for distant-talking speaker identification

Data type Usage Data set
Training data To train the DNN and GMM 100 (speakers) × 5 (utterances) × 3 (environments)
Development data To determine the settings of the DNN (layers, batches, etc.) in the experimental step Same as above
Test data To test the speakers in this dataset in the evaluation step 100 (speakers) × 20 (utterances) × 5 (environments)