Modified MFCCs for robust speaker recognition

摘要：

Mel-scale frequency cepstrum coefficients (MFCCs) arc commonly used featues in speaker recognition systems, but MFCC values are not very robust in the presence of noise, thus, the modified MFCCs (named as SMN-CMN- MFCC) based on the general noisy speech model is proposed in this paper, which uses spectrum mean normalization (SMN) to suppress the additive noise, and uses cepstral mean normalization (CMN) to remove the effect of convolutional noise. Theoretical analyses show that the combination of SMN and CMN can inhibit additive and convolutional noise at the same time. To verify the performance of the SMN-CMN-MFCC, we have conducted some speaker recognition tests. With the same convolutional noise component, the additive white noise experiments and the additive factory noise experiments showthat SMN-CMN-MFCC provides 10.5％ and 9.6％ relative improvement than the conventional MFCC and AM FCC features, respectively.

关键词： Mel-scale Frequency Cepstral Coefficients feature extraction speaker recognition

作者: Wang Hong Pan Jingui Wang Hong

作者单位: State Key Laboratory for Novel Software Technology,Nanjing University Nanjing, China Institute of Computer Application and Research, Changji University Changji, China

会议类型: 国际会议

会议名称: 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems(2010 IEEE 智能计算与智能系统国际会议 ICIS 2010)

会议地点: 厦门

会议语种:英文

页码: 276-279

在线出版日期: 2010-10-29（万方平台首次上网日期，不代表论文的发表时间）