  Standard probabilistic linear discriminant analysis (PLDA) for speaker
recognition assumes that the sample's features (usually, i-vectors) are given
by a sum of three terms: a term that depends on the speaker identity, a term
that models the within-speaker variability and is assumed independent across
samples, and a final term that models any remaining variability and is also
independent across samples. In this work, we propose a generalization of this
model where the within-speaker variability is not necessarily assumed
independent across samples but dependent on another discrete variable. This
variable, which we call the channel variable as in the standard PLDA approach,
could be, for example, a discrete category for the channel characteristics, the
language spoken by the speaker, the type of speech in the sample
(conversational, monologue, read), etc. The value of this variable is assumed
to be known during training but not during testing. Scoring is performed, as in
standard PLDA, by computing a likelihood ratio between the null hypothesis that
the two sides of a trial belong to the same speaker versus the alternative
hypothesis that the two sides belong to different speakers. The two likelihoods
are computed by marginalizing over two hypothesis about the channels in both
sides of a trial: that they are the same and that they are different. This way,
we expect that the new model will be better at coping with same-channel versus
different-channel trials than standard PLDA, since knowledge about the channel
(or language, or speech style) is used during training and implicitly
considered during scoring.
