  Language identification is the task of automatically determining the identity
of a language conveyed by a spoken segment. It has a profound impact on the
multilingual interoperability of an intelligent speech system. Despite language
identification attaining high accuracy on medium or long utterances(>3s), the
performance on short utterances (<=1s) is still far from satisfactory. We
propose a BERT-based language identification system (BERT-LID) to improve
language identification performance, especially on short-duration speech
segments. We extend the original BERT model by taking the phonetic
posteriorgrams (PPG) derived from the front-end phone recognizer as input. Then
we deployed the optimal deep classifier followed by it for language
identification. Our BERT-LID model can improve the baseline accuracy by about
6.5% on long-segment identification and 19.9% on short-segment identification,
demonstrating our BERT-LID's effectiveness to language identification.
