Language Identification Using Minimum Linguistic Information

Automatic spoken language identification is the problem of identifying the language being spoken from a sample of speech by an unknown speaker. Current language identification systems vary in their complexity. The systems that use higher level information have the best performance. Nevertheless, that information is hard to collect for each new language. In this work, we present a state of the art language identification system, which uses very little linguistic information, and so easily extendable to new languages. In fact, the presented system needs only one language specific phone recogniser (in our case the Portuguese one), and is trained with speech from each of the other languages. We studied the problem of language identification in the context of the European languages (including, for the first time, European Portuguese), which allowed us to study the effect of language proximity in Indo-European languages. The results reveal a significant impact on the identification of some languages. With the SpeechDat-M corpus, with 6 European languages (English, French, German, Italian, Portuguese and Spanish) our system achieved an identification rate of about 80% on 5-second utterances.