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Robust Local Scoring Function for Text-Independent Speaker Verification

9 years 7 months ago
Robust Local Scoring Function for Text-Independent Speaker Verification
Traditionally, the Universal Background Model (UBM) is viewed as the background model of the entire acoustic feature space. We propose a novel interpretation of the UBM model, and consider it as a mapping function that transforms the variable length observations (speech utterances) into a fixed dimensional feature vector (sufficient statistics). After this mapping, a similarity measurement is computed on the fixed dimensional features. With this novel interpretation, we proposed a new similarity measurement which produces more than 10% relative improvement over the conventional UBM-MAP framework in both equal error rate and detection cost function.
Ming Liu, Thomas S. Huang, Zhengyou Zhang
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2006
Where ICPR
Authors Ming Liu, Thomas S. Huang, Zhengyou Zhang
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