Exemplar based language recognition method for short-duration speech segments

摘要

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 new method. This borrows a vector quantization based representation from image classification methods, and constructs the exemplar space using the popular i-vector representation of short duration speech segments. A mapping function is then defined to build the new representation. To evaluate the effectiveness of our proposed method, we conduct extensive experiments on the NIST LRE2007 dataset. The experimental results demonstrate improved performance for short duration speech segments.