基于EMD和交叉熵的语音端点检测算法

摘要

In view of the problem that speech endpoint detection based on Empirical Mode Decomposition(EMD)loses its accuracy and adaptive in adverse environments, this paper proposes a novel speech endpoint detection algorithm based on EMD and cross-entropy. EMD decomposition characteristic is analyzed that probability distribution of white noise in each Intrinsic Mode Functions(IMF)is identified and unrelated to noise amplitude. Since probability distribution of white noise is different from that of speech signal, cross-entropy is used to reflect the difference of speech-frames and noise-frames. EMD-energy feature and cross-entropy are complementary so that they are combined to be a comprehensive determination for speech endpoint detection. Adaptive threshold is set to adapt to negative environments. It catches the changes of noise energy and then it is self-updated to improve accuracy in speech endpoint detection. Simulation results indicate that it is effective and superior in the presence of low Signal-to-Noise Ratio(SNR)and non-stationary noise.%针对复杂噪声环境下基于经验模态分解（EMD）的端点检测算法准确率低且不能自适应环境问题，提出了一种结合EMD和交叉熵的语音端点检测新算法。算法利用白噪声在各本征模态函数（IMF）中的概率分布是既定的且与幅值无关的EMD分解特性，将衡量语音帧与噪声帧概率分布差异性的交叉熵特征与EMD能量特征相结合，设置自更新检测阈值，实现复杂噪声环境下的语音端点检测。仿真实验证实了该方法在低信噪比以及非平稳噪声情况下具有显著的有效性和优越性。