Datasets:

google
/

svq

@@ -52,6 +52,7 @@ Specifically, text prompts and recording environments were randomly allocated ac
 While this approach promotes a rich variety of conditions, it introduces a complexity for traditional data splitting:
 creating partitions that ensure no overlap of speakers and no overlap of text material between splits (a common best practice)
 would lead to a substantial data reduction, estimated at around 40% of the total recordings.
 The primary goal guiding this release strategy is to maximize the utility and volume of the data available to users.
 Therefore, to avoid this significant data loss and provide the fullest possible dataset, the data is released in its entirety
 as an undivided evaluation set. Users intending to train models with this data will need to devise and implement their own

 While this approach promotes a rich variety of conditions, it introduces a complexity for traditional data splitting:
 creating partitions that ensure no overlap of speakers and no overlap of text material between splits (a common best practice)
 would lead to a substantial data reduction, estimated at around 40% of the total recordings.
 The primary goal guiding this release strategy is to maximize the utility and volume of the data available to users.
 Therefore, to avoid this significant data loss and provide the fullest possible dataset, the data is released in its entirety
 as an undivided evaluation set. Users intending to train models with this data will need to devise and implement their own