Language identification in code-switching speech using word-based lexical model

In this paper, a language identification (LID) task is described on Mandarin/Taiwanese code-switching utterances. The proposed word-based lexical model of this LID system integrates acoustic, phonetic and lexical cues. The first two cues are obtained from a large vocabulary continuous speech recognition (LYCSR) system, and the last one is trained for a word-based lexical model. The lexical model is used to identify languages according to the frequency and context of each word by given a sequence of words recognized by the LVCSR system. Because the switching unit in the code-switching speech is a word, the experiments showed that, by using a word-based lexical model, 16% relative reduction of classification errors was achieved compared with that in those LVSCR-based LID systems.