中文摘要：

提出了一种融合统计模型和经验模态分解（EMD）的宽带话音增强方法。该方法首先用统计模型增强算法消除含噪话音中的主要噪声成分，然后用一种基于活动话音检测（VAD）的EMD增强算法做后处理进一步抑制残留噪声，从而使以上2种方法的优点有效地结合。在ITu-TG．160标准下对算法进行了性能测试，测试结果表明，与经典的统计模型方法相比，在不同强度的背景噪声下，增强话音的信噪比提高都较为明显。同时，在低信噪比情况下，该方法能有效抑制增强话音高频部分的音乐噪声，提高了听觉舒适度。

英文摘要：

A combined wideband speech enhancement method based on statistical model and empirical mode decomposi- tion （EMD） was proposed. First, statistical model was used to eliminate the main noise component in noisy speech. Then, the residual noise was further suppressed by a post-processing module which is a speech enhancement atgorithm with voice ac- tivity detection （VAD） based on EMD. The advantages of the two methods were combined effectively. The performance of the proposed method was evaluated under the standard of ITU-T GI60. The experimental results indicate that the algorithm is more effective for improving the SNR in the different noise environments than classical statistical model approach. Meanwhile, in low SNR conditions, musical noise is reduced effectively, and the speech sounds more comfortable.