This paper presents a cluster-based feature transformation technique for telephone-based speaker verification when labels of the handset types are not available during the training phase. The technique combines a cluster selector with cluster-dependent feature transformations to reduce the acoustic mismatches among different handsets. Specifically, a GMM-based cluster selector is trained to identify the cluster that best represents the handset used by a claimant. Handset distorted features are then transformed by cluster-specific feature transformation to remove the acoustic distortion before being presented to the clean speaker models. Experimental results show that cluster-dependent feature transformation with number of clusters larger than the actual number of handsets can achieve a performance level very close to that achievable by the handset-based transformation approaches.
|Original language||English (US)|
|Number of pages||9|
|Journal||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|State||Published - Dec 1 2003|
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)