  The standard approach to mitigate errors made by an automatic speech
recognition system is to use confidence scores associated with each predicted
word. In the simplest case, these scores are word posterior probabilities
whilst more complex schemes utilise bi-directional recurrent neural network
(BiRNN) models. A number of upstream and downstream applications, however, rely
on confidence scores assigned not only to 1-best hypotheses but to all words
found in confusion networks or lattices. These include but are not limited to
speaker adaptation, semi-supervised training and information retrieval.
Although word posteriors could be used in those applications as confidence
scores, they are known to have reliability issues. To make improved confidence
scores more generally available, this paper shows how BiRNNs can be extended
from 1-best sequences to confusion network and lattice structures. Experiments
are conducted using one of the Cambridge University submissions to the IARPA
OpenKWS 2016 competition. The results show that confusion network and
lattice-based BiRNNs can provide a significant improvement in confidence
estimation.
