3 accepted papers
In this paper we investigate methods to predict word error rates in automatic speech recognition in the presence of unknown noise types, which have not been seen during training. The performance measures operate on phoneme posteriorgrams that are obtained from neural nets. We compare average frame-w…
Robustness of automatic speech recognition (ASR) to acoustic mismatches can be improved by multistream framework. Frequently used approach to combine decisions from individual streams involve training large number of neural networks, one for each possible stream combination. In this work, we propose…
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…