摘要
在语音识别中 ,如何经济地挑选语音训练语料 ,使其覆盖尽可能多的语音现象是一个非常重要的问题 .传统的语音训练语料采用手工挑选后再进行检验和补充的方法 ,此方法难以保证所选语料语音现象的覆盖率 .该文提出了一种自动地从大规模语料库中挑选语料的搜索算法 ,此算法不但能使所选语料覆盖几乎所有语音现象 ,而且能保证训练语料中三音子和类三音子有足够的样本个数 ,使训练数据不过于稀疏 ,为训练正确而可靠的语音模型打下了坚实的基础 .
In speech recognition, the selection of training corpus for robust acoustic modeling which can cover almost all phone phenomena is very important. Traditionally, corpus is selected manually first, and then tested and supplemented, which can't provide sufficient coverage of samples for various statistical modeling methods. An algorithm for automatically selecting the training samples from large scale text corpus is developed in this paper. This algorithm can not only cover almost all phone phenomena but also ensure to include ideal samples of triphones or class triphones and ensure enough data for training, which makes it possible to train acoustic model reliably.
出处
《软件学报》
EI
CSCD
北大核心
2000年第2期271-276,共6页
Journal of Software
基金
国家自然科学基金! (No.6 9835 0 30 )资助
关键词
语音识别
语料选择算法
三音子模型
Speech recognition, model training, triphone, class triphone.
