Update README.md

README.md

@@ -99,7 +99,7 @@ sim = float(torch.matmul(e1,e2.t()))
 Although it is not directly designed for this use case, evaluation on a standard ASV task can be performed with this model. Applied to 
 the [VoxCeleb1-clean test set](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/meta/veri_test2.txt), it leads to an equal error rate (EER, lower denotes a better identification, random prediction leads to a value of 50%) of **10.681%** 
 (with a decision threshold of **0.467**). This value can be interpreted as the ability to identify speakers only with non-timbral cues. A discussion about this interpretation can be
-found in the paper mentioned hereabove, as well as other experiments showing correlations between these embeddings and non-timbral voice attributes.
+found in the paper mentioned hereafter, as well as other experiments showing correlations between these embeddings and non-timbral voice attributes.
 
 Please note that the EER value can vary a little depending on the MAX_SIZE defined to reduce long audios (max 30 seconds in our case).