  Mismatched transcriptions have been proposed as a mean to acquire
probabilistic transcriptions from non-native speakers of a language.Prior work
has demonstrated the value of these transcriptions by successfully adapting
cross-lingual ASR systems for different tar-get languages. In this work, we
describe two techniques to refine these probabilistic transcriptions: a
noisy-channel model of non-native phone misperception is trained using a
recurrent neural net-work, and decoded using minimally-resourced
language-dependent pronunciation constraints. Both innovations improve quality
of the transcript, and both innovations reduce phone error rate of a
trainedASR, by 7% and 9% respectively
