Unsupervised speaker adaptation based on sufficient HMM statistics of selected speakers
Unsupervised speaker adaptation based on sufficient HMM statistics of selected speakers
复制标题
基于所选说话人的充分 HMM 统计数据的无监督说话人自适应
DOI：
10.1109/icassp.2001.940837
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发表时间：
2001
期刊：
影响因子：
--
通讯作者：
K. Shikano
中科院分区：
文献类型：
--
作者：
Shinichi Yoshizawa;Akira Baba;Kanako Matsunami;Yuichiro Mera;M. Yamada;K. Shikano
描述了一种有效的无监督说话人自适应方法。该方法基于（1）选择声学上接近测试说话者的说话者的子集，以及（2）根据所选说话者的数据的先前存储的足够的HMM统计来计算适配的模型参数。在该方法中，只有少量的无监督测试说话人的数据是需要的适应。此外，通过使用所选择的说话者的数据的足够的HMM统计，可以完成快速自适应。与预聚类方法相比，由于该方法的聚类结果是根据测试说话人的数据在线确定的，因此可以得到更优的说话人聚类。实验结果表明，该方法比说话人无关模型下的MLLR有更好的性能。此外，该方法只使用一个无监督的句子发音，而MLLR通常使用十个以上的监督句子发音。
Describes an efficient method for unsupervised speaker adaptation. This method is based on (1) selecting a subset of speakers who are acoustically close to a test speaker, and (2) calculating adapted model parameters according to the previously stored sufficient HMM statistics of the selected speakers' data. In this method, only a few unsupervised test speaker's data are required for the adaptation. Also, by using the sufficient HMM statistics of the selected speakers' data, a quick adaptation can be done. Compared with a pre-clustering method, the proposed method can obtain a more optimal speaker cluster because the clustering result is determined according to test speaker's data on-line. Experimental results show that the proposed method attains better improvement than MLLR from the speaker independent model. Moreover the proposed method utilizes only one unsupervised sentence utterance, while MLLR usually utilizes more than ten supervised sentence utterances.
影响因子：
4.3
作者：
LEGGETTER, CJ;WOODLAND, PC
通讯作者：
WOODLAND, PC