改进的T^2-BIC说话人二级分割算法

Improved Two-stage T^2-BIC Algorithm for Speaker Segmentation

针对传统T2-BIC算法累积误差较大、召回率不高的问题,提出一种改进的T2-BIC说话人二级分割算法。第1级采用改进的滑动窗口检测搜索窗中的T2统计量峰值,利用贝叶斯信息准则(BIC)对峰值进行确认,第2级利用分步解决的思想处理由于BIC可信度过低而漏选的分割点。实验结果表明,与同类算法相比,该算法分割效果较好,准确率、召回率和综合性能都有所提高。

This paper proposes an improved two-stage T2-BIC algorithm for speaker segmentation,because traditional T2-BIC algorithm has the problems of a bigger accumulated error and a lower recall ratio.In the first stage,the peak position of T2 statistic in search window is detected by using improved sliding variable-size analysis window,and Bayesian Information Criterion（BIC） algorithm is used to acknowledge the peaks.In the second stage,the idea of divide-and-conquer is used to detect the missed turns because of low BIC reliability.Experimental result shows that compared with other algorithms,the improved algorithm achieves better performance,and improves the precision,recall and F measure.