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

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