A context-dependent approach for speaker verification using sequential decision

Abstract

This paper is concerned about speaker veri ca tion SV using the sequential probability ratio test SPRT In the SPRT input samples are usually as sumed to be i i d samples from a probability density function because an on line probability computation is required Feature vectors used in speech processing obviously do not satisfy the assumption and there fore the correlation between successive feature vectors has not been considered in conventional SV using the SPRT The correlation can be modeled by the hidden Markov model HMM but unfortunately the HMM can not be directly applied to the SPRT because of statistical dependence of input samples This paper proposes a method of HMM probability computation using the mean eld approximation to resolve this problem where the probability of whole input samples is nominally represented as the product of probability of each sample as if input samples were independent each other