采用双谱特征的语音可懂度评价算法

针对现有的语音可懂度评价方法不能有效地处理信号在多种类型的非线性失真下的变化，提出了一种基于双谱特征的语音可懂度评价(Bispectral Speech Intelligibility Metric，BSIM)算法，用三阶统计量从语音信号的谱图中提取特征。双谱可以检测语音信号中的非线性相位耦合，抑制非高斯信号中的高斯噪声，从而揭示更多隐含于信号内部的有用信息。将本方法与现有的语音可懂度指标进行了比较，结果表明，此方法可以成功地预测线性失真和非线性失真造成的语音可懂度下降，其评价结果与主观可懂度结果具有很高的相关度，对信号失真变化敏感。

Speech intelligibility evaluation algorithm using bispectral features

Aiming at the fact that the existing speech intelligibility evaluation methods cannot effectively deal with the signal changes under various types of nonlinear distortions, a bispectral speech intelligibility metric (BSIM) algorithm based on bispectral features is proposed, which uses third-order statistics to extract features from the spectrogram of speech signal. Bispectrum can detect the nonlinear phase coupling in the speech signal and suppress the Gussian noise in the non-Gussian signal, thereby can reveal more useful information hidden in the signal. This method is compared with existing speech intelligibility indicators. The results show that this method can successfully predict the degradation of speech intelligibility caused by linear distortion and nonlinear distortion. The evaluation result is highly correlated with the subjective intelligibility result and sensitive to signal distortion changes.