Meng-Ge Wang

We don’t have enough information about this author to calculate their statistics. If you think this is an error let us know.
Learn More
Our previous work has shown that Deep Bottleneck Features (DBF), generated from a well-trained Deep Neural Network (DNN), can provide high performance Language Identification (LID) when Total Variability (TV) modelling is used for a back-end. This may largely be attributed to the powerful capability of the DNN for finding a frame-level representation which(More)
This paper proposes a novel exemplar-based language recognition method for short duration speech segments. It is known that language identity is a kind of weak information that can be deduced from the speech content. For short duration speech segments, the limited content also leads to a large intra-language variability. To address this issue, we propose a(More)
  • 1