Improving mispronunciation detection using machine learning

Author

Chen, Yuqiang ; Huang, Chao ; Soong, Frank

Author_Institution

Dept. of Comput. Sci. & Eng., Shanghai Jiao Tong Univ., Shanghai

fYear

2009

fDate

19-24 April 2009

Firstpage

4865

Lastpage

4868

Abstract

In this paper, we investigate the problem of mispronunciation detection by considering the influence of speaker and syllables. Machine learning techniques are used to make our method more convenient and flexible for new features, such as syllables normalization. The experimental results on our database, consisting of 9898 syllables pronounced by 100 speakers, show the effectiveness of our method by reducing the average false acceptance rate (FAR) by 42.5% using data set generated by model without adaptation to observation set and reducing average FAR by 32.5% using data set generated by model with adaptation to observation set.

Keywords

computer aided instruction; learning (artificial intelligence); speech processing; Mandarin; automatic mispronunciation detection; computer assisted language learning; database; false acceptance rate; machine learning; syllables normalization; Adaptation model; Asia; Chaos; Computer science; Hidden Markov models; Knowledge engineering; Learning systems; Machine learning; Machine learning algorithms; Support vector machines; Automatic Mispronunciation Detection (AMD); Computer Aided Language Learning (CALL); Machine Learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on

Conference_Location

Taipei

ISSN

1520-6149

Print_ISBN

978-1-4244-2353-8

Electronic_ISBN

1520-6149

Type

conf

DOI

10.1109/ICASSP.2009.4960721

Filename

4960721