摘要
针对说话人语音数据在网络传输过程中的丢失问题,该文提出了一种基于Lagrangian插值的分组恢复方法,评估了丢失帧的实际位置,效果良好,改进了GMM识别算法,分析了一种基于GMM-DM的识别算法,克服了数据丢失对系统识别率的影响。实验结果表明,Lagrangian插值分组恢复方法和GMM-DM识别算法,在丢包率比较大时,可以减小丢帧而造成的负面影响,在训练数据不充分时,提高了系统的识别率。
Aiming at problem of packets loss, this paper proposes a method of lost packets compensation based on Lagrangian interpolation and a new classifier GMM-DM. The algorithm improves performance of GMM classifier when training data is inadequate due to the packets loss during transportation. Experiments show that compensation based on Lagrangian interpolation and GMM-DM new classifier could obtain better results than traditional methods when the ratio of lost packets is relatively high.
出处
《计算机工程》
CAS
CSCD
北大核心
2007年第15期205-206,229,共3页
Computer Engineering
