Integrating acoustic, prosodic and phonotactic features for spoken language identification

The fundamental issue of the automatic language identification is to explore the effective discriminative cues for languages. This paper studies the fusion of five features at different level of abstraction for language identification, including spectrum, duration, pitch, n-gram. phonotactic, and bag-of-sounds features. We build a system and report test results on NIST 1996 and 2003 LRE datasets. The system is also built to participate in NIST 2005 LRE. The experiment results show that different levels of information provide complementary language cues. The prosodic features are more effective for shorter utterances while the phonotactic features work better for longer utterances. For the task of 12 languages, the system with fusion of five features achieved 2.38% EER for 30-sec speech segments on NIST 1996 dataset.