Publication Details
Towards Machines That Know When They Do Not Know: Summary of Work Done at 2014 FREDERICK JELINEK MEMORIAL WORKSHOP
Burget Lukáš, doc. Ing., Ph.D. (DCGM)
Cohen Jordan (FIT)
Dupoux Emmanuel (FIT)
Feldman Naomi (FIT)
Godfrey John (FIT)
Khudanpur Sanjeev
Maciejewski Matthew
Mallidi Sri Harish (FIT)
Menon Anjali (FIT)
Ogawa Tetsuji
Peddinti Vijayaditya
Rose Richard
Stern Richard (FIT)
Wiesner Matthew (FIT)
Veselý Karel, Ing., Ph.D. (DCGM)
Performance monitoring, confidence estimation, multistream recognition of speech
In this paper we have proposed and investigated several new techniques for predicting accuracy of estimation of posterior probabilities of speech sounds on previously unseen data, and have shown the feasibility of this task.
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
@inproceedings{BUT119897,
author="Hynek {Heřmanský} and Lukáš {Burget} and Jordan {Cohen} and Emmanuel {Dupoux} and Naomi {Feldman} and John {Godfrey} and Sanjeev {Khudanpur} and Matthew {Maciejewski} and Sri Harish {Mallidi} and Anjali {Menon} and Tetsuji {Ogawa} and Vijayaditya {Peddinti} and Richard {Rose} and Richard {Stern} and Matthew {Wiesner} and Karel {Veselý}",
title="Towards Machines That Know When They Do Not Know: Summary of Work Done at 2014 FREDERICK JELINEK MEMORIAL WORKSHOP",
booktitle="Proceedings of 2015 IEEE International Conference on Acoustics, Speech and Signal Processing",
year="2015",
pages="5009--5013",
publisher="IEEE Signal Processing Society",
address="South Brisbane, Queensland",
doi="10.1109/ICASSP.2015.7178924",
isbn="978-1-4673-6997-8",
url="https://ieeexplore.ieee.org/document/7178924"
}