In addition , we proposed the posterior union model ( pum ) , which improves over the conditional union model by retaining the advantage of requiring no identity of the noisy components , and by additionally offering a means of optimally estimating the model order , therefore enhancing the capability of the model for dealing with nonstationary noise 最终本文提出了语音增强结合pum模型的一种新的语音抗噪方法,并且基于这种新方法我们从高识别率和低成本较高识别率两方面出发,构建了改进型ctranc结合pum新模型和改进型重复wiener滤波结合pum新模型。