Robust Recognition of Mandarin Vowels by Articulatory Manners

This paper proposes a robust classifier for Mandarin vowels considering articulatory manners (AMs) which include the height of the body of the tongue, the front-back position of the tongue, and the degree of lip rounding. Firstly, the articulatory manners of each vowel are encoded to a 3-dimension vector pattern. Then, acoustic features are extracted and mapped to the articulatory manner vector by ELM. Finally, the nearest vowel to the articulatory manner vector is chosen as the recognized result. Comparison between our method and the direct method without considering the articulatory manners shows that the proposed method has an improvement of 7.1 percentage points. Tests with three kinds of noisy data in the Aurora-4 show it also outperforms the normal method with an about a gain of about 4 percentage points.