Speaker recognition using matched filters
Nowadays state-of-the-art speaker recognition systems obtain quite accurate results for both text-independent and text-dependent tasks as long as they are trained on a fair amount of development data from the target domain, and as long as the target data is clean. In this work we investigate the use of matched filters for speaker recognition in the framework of a small in-domain development data. We show how a matched filter can be optimized to maximize SNR (signal to noise ratio) when the noise component includes both intra-speaker variability and center/mean hyper-parameter variability. The proposed method generalizes our previous method named score stabilization and obtains significant speaker recognition error reductions.