Speaker verification with non-audible murmur segments by combining global alignment kernel and penalized logistic regression machine
نویسندگان
چکیده
We investigate a novel method for speaker verification with non-audible 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.
منابع مشابه
Speaker verification with non-audible murmur segments
We propose a speaker verification method using non-audible murmur (NAM) segments, which are different from normal speech and hard for other people to catch them. To use NAM, we therefore take a text-dependent verification strategy in which each user utters her/his own keyword phrase and utilize not only speaker-specific but also keyword-specific acoustic information. We expect this strategy to ...
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