Identification of Scandinavian Languages from Speech Using Bottleneck Features and X-Vectors

This work deals with identification of the three main Scandinavian languages (Swedish, Danish and Norwegian) from spoken data. For this purpose, various state-of-the-art approaches are adopted, compared and combined, including i-vectors, deep neural networks (DNNs), bottleneck features (BTNs) as well as x-vectors. The best resulting approaches take advantage of multilingual BTNs and allow us to identify the target languages in speech segments lasting 5 s with a very low error rate around 1%. Therefore, they have many practical applications, such as in systems for transcription of Scandinavian TV and radio programs, where different persons speaking any of the target languages may occur. Within identification of Norwegian, we also focus on an unexplored sub-task of distinguishing between Bokmal and Nynorsk. Our results show that this problem is much harder to solve since these two language variants are acoustically very similar to each other: the best error rate achieved in this case is around 20%.