Phonetic Mispronunciation Detection Based on GMM and VOT
Huang Zhong-wei · Science Technology and Engineering · 2013
Combining Gaussian mixture model(GMM) and voice onset time(VOT) features,a novel Mandarin phonetic mispronunciation detection approach is proposed which combines the spectral features and the prosodic features.GMMs are used to model the Mel-frequency cepstral coefficients(MFCCs) for all the phonemes and evaluate the pronunciation of most of the phonemes directly.For some consonants which are difficult to distinguish,prosodic features reflecting the VOT are extracted and used to achieve the classification.Mandarin phonetic mispronunciation detection experiments on limited data indicate the significant improvement.So,the new framework is quite helpful for the implementation of independent Putonghua training by the adult hearing-impaired people.