Title :
Towards machines that know when they do not know: Summary of work done at 2014 Frederick Jelinek Memorial Workshop
Author :
Hermansky, Hynek ; Burget, Lukas ; Cohen, Jordan ; Dupoux, Emmanuel ; Feldman, Naomi ; Godfrey, John ; Khudanpur, Sanjeev ; Maciejewski, Matthew ; Mallidi, Sri Harish ; Menon, Anjali ; Ogawa, Tetsuji ; Peddinti, Vijayaditya ; Rose, Richard ; Stern, Richar
Author_Institution :
Johns Hopkins Univ., Baltimore, MD, USA
Abstract :
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.
Keywords :
neural nets; probability; speech recognition; 2014 Frederick Jelinek Memorial Workshop; nonlinear deep neural network probability estimator; speech recognition; Accuracy; Correlation; Estimation; Noise; Speech; Speech recognition; Training data; Performance monitoring; confidence estimation; multistream recognition of speech;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on
Conference_Location :
South Brisbane, QLD
DOI :
10.1109/ICASSP.2015.7178924
