Spectral subtraction in model distance maximizing framework for robust speech recognition

摘要：

This paper has presented a novel discriminative parameters calibration approach based on the Model Distance Maximizing (MDM) to improve the performance of our previous proposed robustness method named spectral subtraction (SS) in likelihoodmaximizing framework.In the previous work,for adjusting the spectral over-subtraction factor of SS,conventional ML approach is used that only utilizes the true model without considering other confused models.This makes it very probably to reach a suboptimal solution.While in MDM,by maximizing the dissimilarities among models,the performance of our speech recognizer-based spectral subtraction method could be further improved.Experimental results based on FarsDat database have demonstrated that MDM approach outperformed ML in term of recognition accuracy.

作者: Bagher BabaAli Hossein Sameti Mehran Safayani

作者单位: Department of Computer Science,Islamic Azad University-Dashtestan Branch,Borazjan,Iran Computer Engineering Department,Sharif University of Technology,Tehran,Iran. Computer Engineering Department,Sharif University of Technology,Tehran,Iran

会议类型: 国际会议

会议名称: 9th International Conference on Signal Processing(第九届国际信号处理学术会议)(ICSP08)

会议地点: 北京

会议语种:英文

在线出版日期: 2008-10-26（万方平台首次上网日期，不代表论文的发表时间）