An SVM-based Mandarin pronunciation quality assessment system

Fengpei Ge, Fuping Pan, Changliang Liu, Bin Dong, Shui Duen Chan, Xinhua Zhu, Yonghong Yan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

6 Citations (Scopus)

Abstract

This paper presents our Mandarin pronunciation quality assessment system for the examination of Putonghua Shuiping Kaoshi (PSK) and investigates a novel Support Vector Machine (SVM) based method to improve its assessment accuracy. Firstly, an selective speaker adaptation module is introduced, in which we select well pronounced speech from results of the first-pass automatic pronunciation scoring as the adaptation data, and adopt Maximum Likelihood Linear Regression to update the acoustic model (AM). Then, compared with the traditional triphone based AM, the monophone based AM is studied. Finally, we propose a new method of incorporating all kinds of posterior probabilities using SVM classifier. Experimental results show that the average correlation coefficient between machine and human scores is improved from 83.72% to 85.48%. It suggests that the two methods of selective speaker adaptation and multi-model combination using SVM are very effective to improve the accuracy of pronunciation quality assessment.
Original languageEnglish
Title of host publicationAdvances in Intelligent and Soft Computing
PublisherSpringer Verlag
Pages255-265
Number of pages11
ISBN (Electronic)9783642012150
Publication statusPublished - 1 Jan 2009
Event6th International Symposium of Neural Networks, ISNN 2009 - Wuhan, China
Duration: 26 May 200929 May 2009

Publication series

NameAdvances in Intelligent and Soft Computing
Volume56
ISSN (Print)1867-5662
ISSN (Electronic)1860-0794

Conference

Conference6th International Symposium of Neural Networks, ISNN 2009
Country/TerritoryChina
CityWuhan
Period26/05/0929/05/09

Keywords

  • CALL
  • Pronunciation Quality Assessment
  • Speaker Adaptation
  • Speech Recognition
  • SVM

ASJC Scopus subject areas

  • Computer Science(all)

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