| Autor: |
Richard M. Stern, Matthew Wiesner, Anjali Menon, Vijayaditya Peddinti, John J. Godfrey, Richard Rose, Sri Harish Mallidi, Hynek Hermansky, Karel Vesely, Jordan Cohen, Emmanuel Dupoux, Sanjeev Khudanpur, Lukas Burget, Tetsuji Ogawa, Matthew Maciejewski, Naomi H. Feldman |
| Rok vydání: |
2015 |
| Předmět: |
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| Zdroj: |
ICASSP |
| Popis: |
A group of junior and senior researchers gathered as a part of the 2014 Frederick Jelinek Memorial Workshop in Prague to address the problem of predicting the accuracy of a nonlinear Deep Neural Network probability estimator for unknown data in a different application domain from the domain in which the estimator was trained. The paper describes the problem and summarizes approaches that were taken by the group1. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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