Similarity or distance metric to calculate scores?

[Nada-gh]

Hello, I noticed you used the pairwise.distance function to calculate the similarity scores for utterances pairs. According to the official documentation of that function, it calculates the distance (which is different from similarity) between feature vectors. Could you please explain. Thank you.

[zabir-nabil]

I think you can use a similarity function too, for example,

dist = F.cosine_similarity(ref_feat.unsqueeze(-1), com_feat.unsqueeze(-1).transpose(0,2)).detach().cpu().numpy()
score = numpy.mean(dist) # see, now, we don't multiply by -1 as it's a similarity based score not a distance

Cosine sometimes gives slightly better results.

[Jungjee]

Closing as this issue has been inactive over six months. When the speaker embeddings are normalised to have a length of 1, simple dot product gives their similarity, which can be thought as 1 - distance.