0:00:29 university espain speaker recognition i-vector speaker recognition PLDA to get the parameters of the PLDA, we need to do the point estimates of the parameters maximum likelihood supervise plenty of data development data from the PLDA considers i-vector decompose where the prior is Gaussian to use this model a large number of data if we don't have a large of data, we are forced to speaker vector where the prior for y is Gaussian Gaussian in this case we need less so if we have for example twenty a number of dimension of speaker vector ninety in the Bayesian approach for the parameters we are assumed they are priors on the model parameters and then we compute the posterior given the i-vectors and so methods compute the posterior prior in this case we compute the posterior from now on we call this prior and finally we take by computing their expected values given the target posterior to get the posterior of the model parameters solutions what we do is they compose assume model parameters then we compute in a cyclic fashion and finally we approximate is the number of speakers in the database and the posterior for the for the channels is the number of the segments in the then we can compute for the target data set from the original data set to the target data set we can compute the weight of the prior target data to do that we should modify the prior distribution the weight prior has dependent of the number of the speakers that we have in the last data set so we change the parameters we want to multiply the weight prior we have need to modify the alpha these two parameters but at the same time, they give the same expectation values for we can do the same with the prior of w and the finally for the number of speakers and the number of segments effective number of speakers and segments of the prior Gaussian we are going to compare out methods the normalization is that do centering and whitening to make more Gaussians fixing Gaussian unitary hypersphere to reduce the data set now I explain the data set data set this is data set we will use similar to the telephone channels that contains 30 male and 30 female data has the similar conditions conditions two to three minutes data set with large we use this five that contains more than five hundred males and seven hundred females and it has variety of channels speaker verification we got twenty MFCC's plus delta and we build the system we use the normalization too the parameters and finally we used s norm score normalization with cohorts from the first here we compare we can see improvement we can see that the prior distribution we compare for instance the first line and the last line equal error rate forty percent for males and fourteen percent for females for min d c f improvement of twelve percent for males and forty six percent for females here it is a table compare difference parameters we can see improvement here we show length normalization with s norm and without s norm when we use improvement using i-vector but not as much as we can see too that in this data set vector normalization better or here we show some improvements and for females finally we see that we can see that without normalization finally the conclusions we have developed a method to adapt a p l d a i-vector classifier from a domain with a large amount of development data to a domain with scarce development data we have conducted experiments we can see this technique improves the performance of the system and these improvement mainly comes from the adaptation of the channel matrix w we have compared this method with the length normalization we have better results we have discussed length normalization as future work Bayesian adaptation of the u b m and the i-vector extractor no the i-vector length means not the dimensional of the i-vector maybe we can do the same as we have more norm data