The desired speech detector plays an important role for controlling the speech distortion in spatial filtering based speech enhancement algorithms. However, the conventional complex coherence(CC) based algorithms can only distinguish the coherent speech and diffuse noise. To improve the performance on the scenarios that both the coherent interference and diffuse noise are present, we propose a directional coherence function(DCF) based detector in this paper. Based on a pair of complementary filters which can suppress the diffuse noise and the coherent interference respectively, the DCF is computed as the normalized correlation between the filters’ outputs. Meanwhile, the filters are solved by convex programming method and satisfy the constraints on speech distortionless and white noise gain(WNG). Consequently, the value of DCF will be close to 1 only for the desired speech dominated time-frequency(T-F) bins and much smaller than 1 for the noise or interference dominated T-F bins. To extract the desired speech, the DCF based Desired Speech Presence Probability(DSPP) is used to control the adaptation in general sidelobe canceler(GSC), and subsequently used as the post-filtering weight. Systematical experiments on several scenarios show that the proposed algorithm achieves significantly and consistently better noise suppression performance than the narrowband direction-of-arrival(DOA) estimates based algorithms.

期望的语音检测器在控制基于空间滤波的语音增强算法中的语音失真中起着重要的作用。但是，传统的基于复杂相干性（CC）的算法只能区分相干语音和散射噪声。为了提高在同时存在相干干扰和散射噪声的情况下的性能，本文提出了一种基于定向相干函数（DCF）的检测器。基于一对分别可以抑制漫射噪声和相干干扰的互补滤波器，将DCF计算为滤波器输出之间的归一化相关性。同时，采用凸编程方法对滤波器进行求解，满足了对语音无失真和白噪声增益（WNG）的约束。所以，仅对于期望的语音主导的时频（TF）区间，DCF的值将接近于1，而对于噪声或干扰主导的TF区间，DCF的值将小于1。为了提取期望的语音，基于DCF的期望语音存在概率（DSPP）用于控制通用旁瓣消除器（GSC）中的自适应，并随后用作后滤波权重。在几种情况下的系统实验表明，与基于窄带到达方向（DOA）估计的算法相比，所提出的算法在噪声抑制性能上一直显着提高。基于DCF的期望语音存在概率（DSPP）用于控制通用旁瓣消除器（GSC）中的自适应，并随后用作后滤波权重。在几种情况下的系统实验表明，与基于窄带到达方向（DOA）估计的算法相比，所提出的算法在噪声抑制性能上始终具有显着提高。基于DCF的期望语音存在概率（DSPP）用于控制通用旁瓣消除器（GSC）中的自适应，并随后用作后滤波权重。在几种情况下的系统实验表明，与基于窄带到达方向（DOA）估计的算法相比，所提出的算法在噪声抑制性能上一直显着提高。