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Contains all the data: Bentsen, T., T.May, A. A. Kresnner, and T. Dau. The benefit of combining a deep neural network architecture with ideal ratio mask estimation in computational speech segregation to improve speech intelligibility. PLOS ONE., in review. There are two folders: WRSs: the Word Recognition Scores (WRSs) from the listener study. The matrix has dimensions 9 conditions x 20 subjects. Data is ordered corresponding to the following condition order: 'UP', 'GMM', 'GMM (3 subbands)', 'GMM (7 subbands)', 'GMM (11 subbands)', 'DNN (IBM)'; 'DNN (IBM, 40 ms)'; 'DNN (IRM)'; 'DNN (IRM, 40 ms)' Masks: GMM-IBMs: IBMs and estimated IBMs for the models 'GMM', 'GMM (3 subbands)', 'GMM (7 subbands)', 'GMM (11 subbands)' DNN-IBMs: IBMs and estimated IBMs for the models 'DNN (IBM)'; 'DNN (IBM, 40 ms)' DNN-IRMs: IRMs and estimated IRMs for the models 'DNN (IRM)'; 'DNN (IRM, 40 ms)'
Computational speech segregation, deep neural network, ideal binary mask, ideal ratio mask, word recognition scores
Computational speech segregation, deep neural network, ideal binary mask, ideal ratio mask, word recognition scores
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