An Automatic Language Identification (LID) approach is presented. The baseline LID system consists of three parts: (1) hidden Markov model (HMM) based context-independent phone recognizers, (2) language identification score generators and (3) a linear language classifier. The system exploits language-dependent phonotactic constraints and prosodic information. Four methods are proposed to improve the system performance. Two bigram-based interpolated Ngram language models (forward and backward) are used to model the phone sequence constraints of different spoken languages. A contextdependent duration model interpolated by a context-independent duration model is used to capture the duration information. Comparison experiments between the linear classifier and neural network-based final classifiers were conducted. Finally, optimization of language model based on back propagation is proposed. The improved system was evaluated on an 11-language task, and performance reached 13β’3% and 26β’2% (error rate) for utterances averaging 45 s duration and 10 s duration, respectively. Compared with the baseline system performance, it shows the importance of the issues addressed in this paper for language identification.