Missing Data Imputation based on Compressive Sensing for Robust Speaker Identification
In this paper, the method of missing data imputation based on the emergent field of compressive sensing for the front end of a speaker identification system in noisy conditions is investigated. Firstly, noisy speech signals are transformed into Gammatone spectrum by using cochlear filtering; then, unreliable spectral components are reconstructed given an incomplete set of reliable ones; finally, speaker features with auditory model are extracted from reconstructed Gammatone spectral data. Experimental results demonstrate that our method can improve the identification accuracy of speaker identification in noisy environments.
Xianyi Rui
School of Electronic&Information Soochow University Suzhou, China
国际会议
上海
英文
1-4
2010-10-20（万方平台首次上网日期，不代表论文的发表时间）