Emotion mismatch between training and testing is one of the important factors causing the performance degradation of speaker recognition system. In our previous work, a bi-model emotion speaker recognition (BESR) meth...Emotion mismatch between training and testing is one of the important factors causing the performance degradation of speaker recognition system. In our previous work, a bi-model emotion speaker recognition (BESR) method based on virtual HD (High Different from neutral, with large pitch offset) speech synthesizing was proposed to deal with this problem. It enhanced the system performance under mismatch emotion states in MASC, while still suffering the system risk introduced by fusing the scores from the unreliable VHD model and the neutral model with equal weight. In this paper, we propose a new BESR method based on score reliability fusion. Two strategies, by utilizing identification rate and scores average relative loss difference, are presented to estimate the weights for the two group scores. The results on both MASC and EPST shows that by using the weights generated by the two strategies, the BESR method achieve a better performance than that by using the equal weight, and the better one even achieves a result comparable to that by using the best weights selected by exhaustive strategy.展开更多
文摘Emotion mismatch between training and testing is one of the important factors causing the performance degradation of speaker recognition system. In our previous work, a bi-model emotion speaker recognition (BESR) method based on virtual HD (High Different from neutral, with large pitch offset) speech synthesizing was proposed to deal with this problem. It enhanced the system performance under mismatch emotion states in MASC, while still suffering the system risk introduced by fusing the scores from the unreliable VHD model and the neutral model with equal weight. In this paper, we propose a new BESR method based on score reliability fusion. Two strategies, by utilizing identification rate and scores average relative loss difference, are presented to estimate the weights for the two group scores. The results on both MASC and EPST shows that by using the weights generated by the two strategies, the BESR method achieve a better performance than that by using the equal weight, and the better one even achieves a result comparable to that by using the best weights selected by exhaustive strategy.
基金浙江省“尖兵”“领雁”研发攻关计划(2024C01058)浙江省“十四五”第二批本科省级教学改革备案项目(JGBA2024014)+2 种基金2025年01月批次教育部产学合作协同育人项目(2501270945)2024年度浙江大学本科“AI赋能”示范课程建设项目(24)浙江大学第一批AI For Education系列实证教学研究项目(202402)。
