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Mismatched transcriptions have been proposed as a mean to acquireprobabilistic transcriptions from non-native speakers of a language.Prior workhas demonstrated the value of these transcriptions by successfully adaptingcross-lingual ASR systems for different tar-get languages. In this work, wedescribe two techniques to refine these probabilistic transcriptions: anoisy-channel model of non-native phone misperception is trained using arecurrent neural net-work, and decoded using minimally-resourcedlanguage-dependent pronunciation constraints. Both innovations improve qualityof the transcript, and both innovations reduce phone error rate of atrainedASR, by 7% and 9% respectively

Performance Improvements of Probabilistic Transcript-adapted ASR with Recurrent Neural Network and Language-specific Constraints