Automatic pronunciation scoring using learning to rank and DP-based score segmentation

@inproceedings{Chen2010AutomaticPS,
  title={Automatic pronunciation scoring using learning to rank and DP-based score segmentation},
  author={Liang-Yu Chen and Jyh-Shing Roger Jang},
  booktitle={INTERSPEECH},
  year={2010}
}
This paper proposes a novel automatic pronunciation scoring framework using learning to rank. Human scores of the utterances are treated as ranks and are used as the ranking ground truths. Scores generated from various existing scoring methods are used as the features to train the learning to rank function. The output of the function is then segmented by the proposed DP-based method and hence boundaries between clusters can be used to determine the discrete computer scores. Experimental results… CONTINUE READING

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BLLIP 1987-89 WSJ Corpus Release 1”, Linguistic Data Consortium, Philadelphia, http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId= LDC2000T43, assessed on

  • E Charniak
  • 2010
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