In this paper, we propose a supervised single-channel speech enhancement method that combines Kullback-Leibler (KL) divergence-based non-negative matrix factorization (NMF) and a hidden Markov model (NMF-HMM). With the integration of the HMM, the temporal dynamics information of speech signals can be taken into account. This method includes a training stage and an enhancement stage. In the training stage, the sum of the Poisson distribution, leading to the KL divergence measure, is used as the observation model for each state of the HMM. This ensures that a computationally efficient multiplicative update can be used for the parameter update of this model. In the online enhancement stage, a novel minimum mean square error estimator is proposed for the NMF-HMM. This estimator can be implemented using parallel computing, reducing the time complexity. Moreover, compared to the traditional NMF-based speech enhancement methods, the experimental results show that our proposed algorithm improved the short-time objective intelligibility and perceptual evaluation of speech quality by 5% and 0.18, respectively.

中文翻译：

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一种基于非负隐马尔可夫模型和Kullback-Leibler散度的语音增强算法

在本文中，我们提出了一种有监督的单通道语音增强方法，该方法结合了基于 Kullback-Leibler (KL) 散度的非负矩阵分解 (NMF) 和隐马尔可夫模型 (NMF-HMM)。通过 HMM 的集成，可以考虑语音信号的时间动态信息。该方法包括训练阶段和增强阶段。在训练阶段，导致 KL 散度度量的泊松分布的总和被用作 HMM 每个状态的观察模型。这确保了计算上有效的乘法更新可以用于该模型的参数更新。在在线增强阶段，针对 NMF-HMM 提出了一种新颖的最小均方误差估计器。该估计器可以使用并行计算来实现，从而降低时间复杂度。