Using SVM to Improve the Characterization of the Alternative Hypothesis for Speaker Verification

• Main Authors: Guo-hao Huang, 黃國豪
• Other Authors: Yau-tarng Juang
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/61298532665067006587

碩士 === 國立中央大學 === 電機工程研究所 === 98 === This thesis proposes a new verification system to improve the performance for speaker verification. The proposed system combines Weighted Geometric Combination (WGC), Weighted Arithmetic Combination (WAC), Universal Background Model (UBM) and Most Competitive Cohort Model (MAX), and uses Support Vector Machine to generate weight vectors for a new decision function. We calculate the likelihood scores of input utterances’ MFCC with registered speaker model, and build input vectors for training SVM models and testing through WGC and WAC. Also, we proposed a scaling method to adapt the unreasonable likelihood scores. The range of scaling is , and this method and range is shown to improve the system by our experiments. And then we select the Top 60 imposters from total speakers’ likelihood scores by imposter selection. These methods not only can make the training model more robust but also can reduce the time of calculations. The experiments results are based on 128-mixture GMMs, Top 60 imposter selection and scaling range of environment. The proposed system obtains a 2.63% EER and 2.36% DCF improvement over [50], and a 0.61% EER improvement over [51].