Improving mispronunciation detection using machine learning

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.