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中文摘要：
很多麦克风阵列时延估计算法在噪声和混响环境下性能都会下降。该文提出一种基于多路线性预测（multiple linear prediction，MLP）的时延估计算法。通过传递函数比估计来消除通道间传递函数的非对称性，提高信号相关程度；空间预测技术引入了阵列冗余信息，并以相关系数矩阵作为时廷搜索的目标函数，提高时延估计的可靠性。实验结果显示了多路线性预测算法的估计准确率更高，性能更加稳健。与几种经典算法相比，在噪声和混响环境下MLP算法的估计正确率分别提高了5％和30％以上。
英文摘要：
Most time delay estimation methods using microphone arrays suffer from degradation in noisy, reverberating environments. A multiple linear prediction （MLP） algorithm was developed using transfer function ratio estimates to eliminate the asymmetry between different channels as well as correlate the signals more. Spatial prediction using redundant information from the microphone arrays treats the correlation coefficient matrix as the objective function to improve the estimation reliability. Tests show that the algorithm is more accurate and robust. The estimation accuracy is more than 5 better than classic methods in noisy environment and 30% better in reverberating environments.
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Time-frequency cepstral features and combining discriminative training for phonotactic language reco
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