On the use of supra model information from multiple classifiers for robust speaker identification

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.

show abstract

A two-stage scoring method combining world and cohort models for speaker verification

Zhang

Mak²,

He³

2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100)

View full text Add to dashboard Cite

On the use of supra model information from multiple classifiers for robust speaker identification

Cited by 6 publications

References 0 publications

Weighting Scores to Improve Speaker-Dependent Threshold Estimation in Text-Dependent Speaker Verification

Weighting Scores to Improve Speaker-Dependent Threshold Estimation in Text-Dependent Speaker Verification

Feature mapping and recuperation by using elliptical basis function networks for robust speaker verification

A two-stage scoring method combining world and cohort models for speaker verification

Contact Info

Product

Resources

About