In this paper, we study the vocabulary design problem in Uyghur large vocabulary continuous speech recognition (LVCSR). Uyghur is an agglutinative language in which words can be formed by concatenating several suffixes to the stem. As a result, the number of word types in Uyghur is unlimited. If the word is used as the recognition unit, the out-of-vocabulary (OOV) rate will be very large with typical vocabulary sizes of 60k–100k. To avoid this problem, we split words into stems and suffixes and use these sub-words as the recognition units. Speech recognition experiments are performed in two test sets, one including sentences in books and another including sentences in conversations. Compared to the 80k-word baseline, the use of stems and suffixes can alleviate the OOV rate problem dramatically and the best system reduces the word error rate (WER) from 46.5% to 44.5% in the book sentences test set and from 57.6% to 47.5% in the conversation sentences test set.
['Xin Li', 'Shang Cai', 'Jielin Pan', 'Yonghong Yan', 'Yafei Yang']
Large vocabulary Uyghur continuous speech recognition based on stems and suffixes