This paper addresses the problem of speaker verification under reverberant conditions, using only the signal acquired by a single distant microphone. The proposed system combines four different subsystems. Two of them are Gaussian Mixture Model (GMM) based and the other two are Support Vector Machine (SVM) based. The subsystems that use the same type of classifier differ in terms of models: one is trained with clean speech and the other is trained with noisy and reverberant speech obtained through the contamination of the clean data with the measured impulse responses of the room. The results show that the proposed system outperforms each single subsystem under matched or mismatched conditions.
Combination of clean and contaminated GMM/SVM for far-field text-independent speaker verification
Zieger, Christian;Omologo, Maurizio
2008-01-01
Abstract
This paper addresses the problem of speaker verification under reverberant conditions, using only the signal acquired by a single distant microphone. The proposed system combines four different subsystems. Two of them are Gaussian Mixture Model (GMM) based and the other two are Support Vector Machine (SVM) based. The subsystems that use the same type of classifier differ in terms of models: one is trained with clean speech and the other is trained with noisy and reverberant speech obtained through the contamination of the clean data with the measured impulse responses of the room. The results show that the proposed system outperforms each single subsystem under matched or mismatched conditions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
