中文摘要：

针对基于拉普拉斯分布灵活独立分量分析算法收敛速度较慢的问题，提出了一种基于广义伽玛分布的灵活独立分量分析算法，该算法把广义伽玛分布概率密度函数作为语音信号概率密度函数的估计，得到一个更加适合语音信号分离的激活函数。将推导出的激活函数应用于独立分量分析（ICA）的自然梯度算法中进行了计算机仿真实验，验证了算法的收敛性能和分离性能。

英文摘要：

The flexible independent component analysis algorithm based on Laplacian distribution had slow convergence speed. To improve convergence speed of the algorithm, a flexible inde- pendent component analysis algorithm based on generalized gamma distribution was proposed. Taking generalized gamma distribution function as a statistical estimation model for speech sig- nals, a nonlinear activation function more suitable for speech separation was obtained. And on the basis of this function, using this nonlinear activation function in natural gradient based ICA algorithm in computer simulations, we got the results which proved the above mentioned properties.