Language identification with limited resources

Language identification is an important issue in many speech applications. We address this problem from the point of view of classification of sequences of phonemes, given the assumption that each language has its own phonotactic characteristics. In order to achieve this classification, we have to decode the speech utterances in terms of phonemes. The set of phonemes must be the same for all the languages, because the goal is to have a comparable representation of the acoustic sequences. We followed two different approaches using the same acoustic model: we decode the audio using trigrams of sequences of phonemes and equiprobable unigrams of phonemes as language model. Then a classification process based on perplexity is performed.