In speech signal handle, Hidden Markov Model acquires the successful application.
在语音信号处理中,隐马尔可夫模型获得了成功的应用。
Because Hidden Markov models are difficult to manage, theory research of Hidden nonhomogeneous Markov models are basically blankness.
但由于隐非齐次马尔可夫模型难以处理,所以对于隐非齐次马尔可夫模型的理论研究基本上还是空白。
Since the widely used Hidden Markov model (HMM) in speech recognition is first order Markov model, it can not fully model the temporal dependence of speech signal.
由于在语音识别中被广泛应用的隐马尔可夫模型(HMM)是一重马尔可夫模型,它不能充分地描述语音信号的时间相依性。
This thesis introduced in detail theories method of the Hidden Markov Model, outstanding the importance that Hidden Markov Model used in speech signal.
本论文详细地介绍了隐马尔可夫模型的理论方法,突出了语音信号隐马尔可夫模型分析的重要性。