Language Identification (LID) is the process for automatically identifying the language of a given spoken utterance. We have focused in a phonotactic approach in which the system input is the phonemes sequence generated by a speech recognizer (ASR), but instead phonemes we have used phonetic units that contain context information “phone-grams”. In this context, we propose the use of Neural Embeddings (NEs) as features for those phone-grams sequences, which are used as entries in a classical i-Vectors framework to train a multi class logistic classifier. These NEs incorporate information from the neighboring phone-grams in the sequence and model implicitly longer-context information. The NEs have been trained using both, Skip-Gram and Glove Model. Experiments have been carried out on the KALAKA-3 database and we have used Cavg as a metric to compare the systems. We propose as baseline the Cavg obtained using the NEs as features in the LID task, 24,69%. Our strategy to incorporate information from the neighboring phone-grams to define the final sequences contributes obtaining up to 24,3% relative improvement over the baseline using Skip-Gram model and up to 32,4% using Glove model. Finally, fusing our best system with an MFCC-based acoustic i-Vectors system provides up to 34,1% improvement.