In this paper we show how methods for approximating phone
error as normally used for Minimum Phone Error (MPE) discriminative
training, can be used instead as a decoding criterion
for lattice rescoring. This is an alternative to Confusion Networks
(CN) which are commonly used in speech recognition.
The standard (Maximum A Posteriori) decoding approach is a
Minimum Bayes Risk estimate with respect to the Sentence Error
Rate (SER); however, we are typically more interested in
the Word Error Rate (WER). Methods such as CN and our proposed
Minimum Hypothesis Phone Error (MHPE) aim to get
closer to minimizing the expected WER. Based on preliminary
experiments we find that our approach gives more improvement
than CN, and is conceptually simpler.