Update README.md

README.md

@@ -42,7 +42,7 @@ dataset_info:
 
 ## Overview
 
-**HABLA-Augmented** is an augmented version of the [HABLA][https://zenodo.org/records/7370805] audio dataset.
+**HABLA-Augmented** is an augmented version of the [HABLA](https://zenodo.org/records/7370805) audio dataset.
 
 The original HABLA dataset contains spoof and bonafide samples across 5 different Spanish dialects.
 This dataset differs from the original by the procedure applied for obtaining the subsets and application of data augmentation,
@@ -51,11 +51,13 @@ which resulted in a dataset with 4 subsets, as follows:
 * Train (balanced & augmented ; train split) 
 * Validation (balanced ; validation split)
 * Close-test (balanced ; test split)
-* Open-test (unbalanced, unseen speakers and synthesis models ; provided as independent dataset) [HABLA-Open-Test][https://huggingface.co/datasets/Dax99993/habla-open-test]
+* Open-test (unbalanced, unseen speakers and synthesis models ; provided as an independent dataset) [HABLA-Open-Test](https://huggingface.co/datasets/Dax99993/habla-open-test)
 
 ## Open-test
 
-To obtain the open-test subset the original set was split in two subsets by speakers and synthesis models. First, 2 out of the 6 available synthesis models were reserved exclusively for testing. Additionally 20 % of the speakers were selected randomly from each accent and sex. The combination of both subsets generated the open-test subset.
+To obtain the open-test subset the original set was split in two subsets by speakers and synthesis models.
+First, 2 out of the 6 available synthesis models were reserved exclusively for testing.
+Additionally 20 % of the speakers were selected randomly from each accent and sex. The combination of both subsets generated the open-test subset.
 
 ```markdown
 | Model       | # Samples | % Set |
@@ -125,7 +127,7 @@ with proportion of samples (labels) as stated in the table
 
 ## Data augmentation
 
-The [RawBoost][https://arxiv.org/pdf/2111.04433] data augmentation technique was applied to the train subset, applying the following algorithms:
+The [RawBoost](https://arxiv.org/pdf/2111.04433) data augmentation technique was applied to the train subset, applying the following algorithms:
 * (4) Series Convolutive-Impulsive-Stationary noise
 * (5) Series Convolutive-Impulsive noise
 * (6) Series Convolutive-Stationary noise