Abstract
This paper discusses Mandarin vowel pronunciation quality assessment. The phonetic pronunciation quality is traditionally evaluated under the speech recognition framework by the phonetic posterior probability score, which may be computed by normalizing the frame-based posterior probability or be calculated on the phone segment directly. By the first method, we can achieve a human-machine scoring correlation coefficient (CC) of 0.832 for vowel; and by the second, the CC can be up to 0.847. This paper proposes a novel kind of formant feature and applies the feature to the evaluation of vowel: we transform the formant plots on the time-frequency plane to a bitmap and extract its Gabor feature for pattern classification; when use the classification probability for pronunciation assessment, we can get a CC of 0.842. Finally we combine the three scores with various linear or nonlinear methods; the best CC of 0.913 is gotten by using neural network.
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Pan, F., Zhao, Q., Yan, Y. (2007). New Machine Scores and Their Combinations for Automatic Mandarin Phonetic Pronunciation Quality Assessment. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74819-9_101
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DOI: https://doi.org/10.1007/978-3-540-74819-9_101
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74817-5
Online ISBN: 978-3-540-74819-9
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