上海大学学报(英文版)
上海大學學報(英文版)
상해대학학보(영문판)
JOURNAL OF SHANGHAL UNIVERSITY
2002年
4期
331-336
,共6页
feature mapping and recurpuration%elliptical basis function (EBF) networks%speaker verification
The performance of speaker verification systems is often compromised under real-world environments. For example, variations in handset characteristics could cause severe performance degradation. This paper presents a novel method to overcome this problem by using a non-linear handset mapper. Under this method, a mapper is constructed by training an elliptical basis function network using distorted speech features as inputs and the corresponding clean features as the desired outputs. During feature recuperation, clean features are recovered by feeding the distorted features to the feature mapper. The recovered features are then presented to a speaker model as if they were derived from clean speech. Experimental evaluations based on 258 speakers of the TIMIT and NTIMIT corpuses suggest that the feature mappers improve the verification performance remarkably.
