摘要
在传统的隐马尔可夫模型中，模型在某状态停留一定时间的概率随着时间的增长呈指数下降的趋势．文中使用依赖于时间的状态转移概率对状态停留时间予以刻画．首先，在采用相同特征矢量下进行了修改后的隐马尔可夫模型和传统隐马尔可夫模型的比较和分析．其次，对不同特征矢量的组合进行了对比实验．另外，在进行不同参数的组合时，文中考虑了不同特征参数及其维数对观察矢量概率输出的影响．
State duration in HMM is modeled by using time dependent state transition probability.Firstly,the traditional HMM is compared with the modified HMM that has incorporated duration information.Almost the same recognition performance is obtained by both HMMs for trained speaker in multi speaker mode.However,it is found that the modified HMM is more robust than the traditional HMM for untrained speaker in multi speaker mode.So it can be considered that the modified HMM that has incorporated duration information contains more phonetic transition information about the syllable to be recognized.Secondly,a new feature is proposed,which is called KLCEP,and an attempt has been made about how to combine various features such as LPCCEP,ARCEP,and KLCEP.ARCEP has been shown to be more robust for untrained speaker too and KLCEP is helpful to improve the performance of trained speaker.Finally,a high performance is obtained by using LPCCEP+ARCEP+KLCEP as a combined feature vector.In addition,while combining various features,a consideration has also been given about how to adjust their dimensional effect.
出处
《计算机研究与发展》
EI
CSCD
北大核心
1999年第3期257-262,共6页
Journal of Computer Research and Development
