The benefit of combining a deep neural network architecture with ideal ratio mask estimation in computational speech segregation to improve speech intelligibility (Q6698422)
From MaRDI portal
 | This is the item page for this Wikibase entity, intended for internal use and editing purposes. Please use this page instead for the normal view: The benefit of combining a deep neural network architecture with ideal ratio mask estimation in computational speech segregation to improve speech intelligibility |
Dataset published at Zenodo repository.
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | The benefit of combining a deep neural network architecture with ideal ratio mask estimation in computational speech segregation to improve speech intelligibility |
Dataset published at Zenodo repository. |
Statements
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 modelsGMM, GMM (3 subbands), GMM (7 subbands), GMM (11 subbands) DNN-IBMs:IBMs and estimated IBMs for the modelsDNN (IBM); DNN (IBM, 40 ms) DNN-IRMs: IRMs and estimated IRMs for the models DNN (IRM); DNN (IRM, 40 ms)
0 references
17 March 2018
0 references
