Mandarin vowel pronunciation quality evaluation by a novel formant classification method and its combination with traditional algorithms

This paper discusses the vowel pronunciation quality assessment of our computer assisted Mandarin Chinese learning system. Under the speech recognition framework, phonetic pronunciation assessment is usually based on 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. In order to improve the performance, we suggest employing the formant feature of vowel. This paper proposes a novel method to utilize formant: we plot formant candidates of each frame on the time-frequency plane to form a bitmap, and then extract its Gabor feature for pattern classification. When we use the classification probability score for pronunciation assessment, we 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.