We investigate a novel method for speaker verification with nonaudible murmur (NAM) segments. NAM is recorded using a special microphone placed on the neck and is hard for other people to hear. We have already reported a method based on a support vector machine (SVM) using NAM segments to use a keyword phrase effectively. To further exploit keyword-specific features, we introduce a global alignment (GA) kernel and penalized logistic regression machine (PLRM). In the experiments using NAM from 55 speakers, our method achieved an error reduction rate of roughly 60% compared with the SVM-based method using a polynomial kernel.
Bibliographic reference. Okamoto, Hideki / Matsui, Tomoko / Kawanami, Hiromichi / Saruwatari, Hiroshi / Shikano, Kiyohiro (2008): "Speaker verification with non-audible murmur segments by combining global alignment kernel and penalized logistic regression machine", In INTERSPEECH-2008, 1369-1372.