  We present a new method for computing ASR word confidences that effectively
mitigates the effect of ASR errors for diverse downstream applications,
improves the word error rate of the 1-best result, and allows better comparison
of scores across different models. We propose 1) a new method for modeling word
confidence using a Heterogeneous Word Confusion Network (HWCN) that addresses
some key flaws in conventional Word Confusion Networks, and 2) a new score
calibration method for facilitating direct comparison of scores from different
models. Using a bidirectional lattice recurrent neural network to compute the
confidence scores of each word in the HWCN, we show that the word sequence with
the best overall confidence is more accurate than the default 1-best result of
the recognizer, and that the calibration method can substantially improve the
reliability of recognizer combination.
