Abstract:
In this paper we present a confidence estimation system using recurrent neural networks (RNN) and compare it to a traditional multilayered perception (MLP) based system. ...Show MoreMetadata
Abstract:
In this paper we present a confidence estimation system using recurrent neural networks (RNN) and compare it to a traditional multilayered perception (MLP) based system. The ability of RNN to capture sequence information and improve decisions using processed history was main motivation to explore RNN's for confidence estimation. In this paper we also explore two subtle variations of confidence estimator: one that uses objective extracted over the entire sequence for training, and other that uses dynamic programming to decode and estimate confidence on all the words of the sequence jointly. In our experiments, we observed that for a constant false positive (FP) rate of 3% we can secure a relative reduction of 10% in false negative (FN) rate when we replaced a MLP in confidence estimator with a RNN.We also observed that relative gains achieved by a RNN based confidence estimator are directly proportional to the number of word in the utterances.
Published in: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Date of Conference: 19-24 April 2015
Date Added to IEEE Xplore: 06 August 2015
Electronic ISBN:978-1-4673-6997-8