Performance Improvement of Probabilistic Transcriptions with Language-specific Constraints

This article describes a method for reducing the error rate of probabilistic phone-based transcriptions resulting from mismatched crowdsourcing by using language-specific constraints to post-process the phone sequence. In the scenario under consideration, there are no native-language transcriptions or pronunciation dictionary available in the test language; instead, available resources include non-native transcriptions, a rudimentary rule-based G2P, and a list of orthographic word forms mined from the internet. The proposed solution post-processes non-native transcriptions by converting them to test-language orthography, composing with testlanguage word forms, then converting back to a phone string. Experiments demonstrate that the phone error rate of the transcription is reduced, using this method, by 22% on an independent evaluation-test dataset.