Decision tree based tone modeling with corrective feedbacks for automatic mandarin tone assessment

Hsien Cheng Liao*, Jiang Chun Chen, Sen Chia Chang, Ying Hua Guan, Chin Hui Lee

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

We propose a novel decision tree based approach to Mandarin tone assessment. In most conventional computer assisted pronunciation training (CAPT) scenarios a tone production template is prepared as a reference with only numeric scores as feedbacks for tone learning. In contrast decision trees trained with an annotated tone-balanced corpus make use of a collection of questions related to important cues in categories of tone production. By traversing the corresponding paths and nodes associated with a test utterance a sequence of corrective comments can be generated to guide the learner for potential improvement. Therefore a detailed pronunciation indication or a comparison between two paths can be provided to learners which are usually unavailable in score-based CAPT systems.

Original languageEnglish
Title of host publicationProceedings of the 11th Annual Conference of the International Speech Communication Association, INTERSPEECH 2010
PublisherInternational Speech Communication Association
Pages602-605
Number of pages4
Publication statusPublished - 2010

Publication series

NameProceedings of the 11th Annual Conference of the International Speech Communication Association, INTERSPEECH 2010

Keywords

  • Computer aided language learning
  • Computer assisted pronunciation training
  • Feedback
  • Tone assessment

ASJC Scopus subject areas

  • Language and Linguistics
  • Speech and Hearing

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