  State-of-art speaker verification (SV) systems use a back-end model to score
the similarity of speaker embeddings extracted from a neural network model. The
commonly used back-end models are the cosine scoring and the probabilistic
linear discriminant analysis (PLDA) scoring. With the recently developed neural
embeddings, the theoretically more appealing PLDA approach is found to have no
advantage against or even be inferior the simple cosine scoring in terms of SV
system performance. This paper presents an investigation on the relation
between the two scoring approaches, aiming to explain the above
counter-intuitive observation. It is shown that the cosine scoring is
essentially a special case of PLDA scoring. In other words, by properly setting
the parameters of PLDA, the two back-ends become equivalent. As a consequence,
the cosine scoring not only inherits the basic assumptions for the PLDA but
also introduces additional assumptions on the properties of input embeddings.
Experiments show that the dimensional independence assumption required by the
cosine scoring contributes most to the performance gap between the two methods
under the domain-matched condition. When there is severe domain mismatch and
the dimensional independence assumption does not hold, the PLDA would perform
better than the cosine for domain adaptation.
