基于结构化特征语音模型的区别性说话人自适应算法

针对特征语音说话人自适应算法的缺陷,提出了基于结构化特征语音模型的区别性说话人自适应方法.该算法能根据自适应数据量调整自适应参数,并采用基于最大互信息量准则的区别性参数估计方法,进一步提高了自适应性能.有监督自适应的实验结果表明,在仅有一句自适应语句的情况下系统误识率相对下降了6.7%,同时算法表现出了优于特征语音自适应方法的渐进性能.

Discriminative Speaker Adaptation Based on Structural Eigenvoice Model

Regarding the drawbacks of eigenvoice speaker adaptation, a novel adaptation algorithm was proposed, which is called discriminative structural eigenvoice(DSEV). This algorithm can adjust the adaptation parameters with the available amount of adaptation data and discriminatively estimate them under the maximum mutual information(MMI) criterion, which further improves the adaptation performance. It can be shown from the experimental results of supervised adaptation that this algorithm achieves 6.7% relative reduction in error rate with only one adaptation sentence. The proposed method also shows better gradual improvement than eigenvoice with increased amount of adaptation data.