Analysis of mismatched transcriptions generated by humans and machines for under-resourced languages

Van Hai Do, Nancy F. Chen, Boon Pang Lim, Mark Hasegawa-Johnson

Research output: Contribution to journalConference article


When speech data with native transcriptions are scarce in an under-resourced language, automatic speech recognition (ASR) must be trained using other methods. Semi-supervised learning first labels the speech using ASR from other languages, then re-trains the ASR using the generated labels. Mismatched crowdsourcing asks crowd-workers unfamiliar with the language to transcribe it. In this paper, self-training and mismatched crowdsourcing are compared under exactly matched conditions. Specifically, speech data of the target language are decoded by the source language ASR systems into source language phone/word sequences. We find that (1) human mismatched crowdsourcing and cross-lingual ASR have similar error patterns, but different specific errors. (2) These two sources of information can be usefully combined in order to train a better target-language ASR. (3) The differences between the error patterns of non-native human listeners and non-native ASR are small, but when differences are observed, they provide information about the relationship between the phoneme systems of the annotator/source language (Mandarin) and the target language (Vietnamese).

Original languageEnglish (US)
Pages (from-to)3863-3867
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
StatePublished - Jan 1 2016
Event17th Annual Conference of the International Speech Communication Association, INTERSPEECH 2016 - San Francisco, United States
Duration: Sep 8 2016Sep 16 2016



  • Mismatched crowdsourcing
  • Semi-supervised learning
  • Speech recognition
  • Under-resourced languages

ASJC Scopus subject areas

  • Language and Linguistics
  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Modeling and Simulation

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