a good afternoon

so in this at one

allpass in this a new method call between class covariance collection to improve language recognition


the we conducted our experiments on the nist lre two thousand fifteen corpus though see

that the corpus is organized like there are twenty languages and each of them are

grouped into six clusters based on their phonetic similarities like you have a rabbit cluster

which has all the grabbing dialects of english and french all these clusters so we

followed a very interesting thing when we

a lot all the i-vectors when the past into the pca and these are the

first two dimensions of the first two base of pca so we found that

the all these languages are grouped together in the form of clusters and all of

these clusters

so you can see that all the languages going to the chinese cluster they are

grouped together

a belong to be i've been a cluster there are grouped together

so they are wonderful multimodal distribution

and so we so we computed the eigen directions representing this multimodal distribution and we

added them to the lda

initial some improvement in performance

so you wanna

no more you in the post animal is once i welcome you all their and

to get the more details about it things