抄録
Language identification is the technique to identify the language being spoken by an unknown speaker. In this paper, phonotactic information was used as the feature for language identification. In order to obtain phonotactic information, it is required to extract the phoneme sequence from speech data. A template-based non-negative matrix factorization was applied for this purpose. The extracted phoneme sequence was then analyzed to yield n-gram models which may reflect the order in which the phoneme-like categories of speech occur in the language. Language identification was carried out by a support vector machine with the n-gram as the feature vector. It is shown that the identification performance changes with the number of spectrum templates and the order of n-gram, and that the best performance of 98.6% was obtained when the number of spectrum was 13 and the order of n-gram was 3.