Abstract: To accurately capture the variations of real speech spectra，two single stream Dynamic Bayesian Network（DBN） models based on context-dependent triphone：SS-DBN-TRI model and SS-DBN-TRI-CON model，are proposed for continuous speech recognition.SS-DBN-TRI model is an augmentation of Single Stream DBN（SS-DBN） model proposed by Bilmes，the phone variable is replaced by triphone variable generated by inter-word；simultaneously，based on SS-DBN model，a previous phone node and a next phone node of current phone are added，resulting in a new triphone node to describe co-articulary of continuous speech inter-word，new triphone node is associated with observation，with some probabilities modeled by Gaussian Mixture Model.Experiment is done on continuous digit audio database，results show that：SS-DBN-TRI-CON model has the best performance in word recognition.

Key words: Dynamic Bayesian Network（DBN）, speech recognition, triphone, mono-phone, context-dependent

摘要： 考虑连续语音中的协同发音问题，提出基于词内扩展的单流上下文相关三音素动态贝叶斯网络（SS-DBN-TRI）模型和词间扩展的单流上下文相关三音素DBN（SS-DBN-TRI-CON）模型。SS-DBN-TRI模型是Bilmes提出单流DBN（SS-DBN）模型的改进，采用词内上下文相关三音素节点替代单音素节点，每个词由它的对应三音素单元构成，而三音素单元和观测向量相联系；SS-DBN-TRI-CON模型基于SS-DBN模型，通过增加当前音素的前音素节点和后音素节点，构成一个新的词间扩展的三音素变量节点，新的三音素节点和观测向量相联系，采用高斯混合模型来描述，采用数字连续语音数据库的实验结果表明：SS-DBN-TRI-CON具备最好的语音识别性能。

关键词: 动态贝叶斯网络, 语音识别, 三音素, 单音素, 上下文相关