Meng-Ge Wang

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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)
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)
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