diff --git a/UT/CommonVoice/cv.md b/UT/CommonVoice/cv.md
index d0b0ae0..dc3b148 100644
--- a/UT/CommonVoice/cv.md
+++ b/UT/CommonVoice/cv.md
@@ -8,21 +8,25 @@ Multiple versions of the dataset are available, released by Mozilla every few mo
 
 <br>
 
-Here is a matrix with **WER** results and the **time** each model/configuration spent transcribing the dataset:
+Here is a matrix with **WER** results and the **time** each model/configuration spent transcribing the test subset:
 
 |Model|WER|Time|
 |---|---|---|
-|Kaldi_NL|20.7%|8h:15m:54s*|
-|faster-whisper v2|5.6%|1h:58m:37s|
-|*faster-whisper v3*|**4.3%**|1h:55m:20s|
+|[Kaldi_NL](https://github.com/opensource-spraakherkenning-nl/Kaldi_NL)|20.7%|8h:15m:54s*|
+|[faster-whisper v2](https://github.com/SYSTRAN/faster-whisper/)|5.6%|1h:58m:37s|
+|**faster-whisper v3**|**4.3%**|1h:55m:20s|
 |faster-whisper v2 w/ VAD|5.6%|1h:58m:50s|
 |faster-whisper v3 w/ VAD|4.4%|2h:01m:33s|
-|XLS-R FT on Dutch|6.5%|1h:04m:00s|
-|*MMS - 102 languages*|13.4%|**0h:37m:50s**|
-|MMS - 1162 languages|9.5%|0:53m:56s|
+|[XLS-R FT on Dutch](https://huggingface.co/jonatasgrosman/wav2vec2-xls-r-1b-dutch)|6.5%|1h:04m:00s|
+|[**MMS - 102 languages**](https://huggingface.co/facebook/mms-1b-fl102)|13.4%|**0h:37m:50s**|
+|[MMS - 1162 languages](https://huggingface.co/facebook/mms-1b-all)|9.5%|0:53m:56s|
 
 \* Most of the time was spent by the configuration running the speaker diarization module.
 
+<br>
+
+There is a clear discrepancy between the WER scores of the newer ASR models and the baseline which is explained by the fact that data from Common Voice has been used to train the newer models, whereas that is not the case for our baseline.
+
 
 ### Normalization
 
diff --git a/UT/Jasmin/jasmin_res.md b/UT/Jasmin/jasmin_res.md
index 0fc8b7d..65f2052 100644
--- a/UT/Jasmin/jasmin_res.md
+++ b/UT/Jasmin/jasmin_res.md
@@ -1,9 +1,9 @@
 [Go back](./jasmin.md)
 
 ## Jasmin Dutch results
-Here is a matrix with **WER** results of the baseline model, Kaldi_NL, as well as different models tested on the **Dutch** part of the corpus:
+Here is a matrix with **WER** results of the baseline model, Kaldi_NL, as well as different models tested on **Dutch read speech**:
 
-|Model\Dataset|Jasmin_q_1|Jasmin_q_2|Jasmin_q_3|Jasmin_q_4|Jasmin_q_5|
+|Model\Dataset|Native Children|Native Teenagers|Non-native Minors|Non-native Adults|Native Elderly|
 |---|---|---|---|---|---|
 |Kaldi_NL|28.1%|16.2%|43.6%|45.3%|20.9%|
 |Whisper v2|22.6%|18.0%|36.5%|37.3%|22.2%|
@@ -18,7 +18,11 @@ Here is a matrix with **WER** results of the baseline model, Kaldi_NL, as well a
 |MMS - 102 languages|31.6%|20.3%|54.2%|55.1%|23.9%|
 |MMS - 1162 languages|28.9%|20.0%|50.1%|54.0%|28.3%|
 
-|Model\Dataset|Jasmin_p_1|Jasmin_p_2|Jasmin_p_3|Jasmin_p_4|Jasmin_p_5|
+<br>
+
+And for **Dutch conversational speech**:
+
+|Model\Dataset|Native Children|Native Teenagers|Non-native Minors|Non-native Adults|Native Elderly|
 |---|---|---|---|---|---|
 |Kaldi_NL|55.4%|62.4%|69.1%|60.0%|44.0%|
 |Whisper v2|95.8%|107.4%|124.0%|88.1%|61.9%|