Speaker Verification Robust to Talking Style Variation Using Multiple Kernel Learning Based on Conditional Entropy Minimization

We developed a new speaker verification system that is robust to intra-speaker variation. There is a strong likelihood that intraspeaker variations will occur due to changes in talking styles, the periods when an individual speaks, and so on. It is well known that such variation generally degrades the performance of speaker verification systems. To solve this problem, we applied multiple kernel learning (MKL) based on conditional entropy minimization, which impose the data to be compactly aggregated for each speaker class and ensure that the different speaker classes were far apart from each other. Experimental results showed that the proposed speaker verification system achieved a robust performance to intra-speaker variation derived from changes in the talking styles compared to the conventional maximum margin-based system.