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Speaker verification suffers from significant performance degradation on emotional speech. We present an adaptation approach based on maximum likelihood linear regression (MLLR) and its feature-space variant, CMLLR. Our preliminary experiments demonstrate that this approach leads to considerable performance improvement, particularly with CMLLR (about 10% relative EER reduction in average). We also...
GMM-UBM-based speaker verification heavily relies on a well trained UBM. In practice, it is not often easy to obtain an UBM that fully matches acoustic channels in operation. To solve this problem, we propose a novel sequential MAP adaptation approach: by being sequentially updated with data from new enrollments, the UBM learns and converges to the working channel. Our experiments are conducted on...
In this paper, we propose a novel tracking algorithm that combines complementary tracking modules with a new object representation model to balance between stability and adaptivity. To reduce the update error of online tracking, we present three complementary modules (a stable module, a soft stable module, and an adaptive module) and fuse them by using a biased multiplicative criterion. The combination...
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