Update README.md

README.md

@@ -65,6 +65,7 @@ _Note: if the data viewer is not working, use the "example" subset._
 # SUMM-RE 
 
 The SUMM-RE dataset is a collection of transcripts of French conversations, aligned with the audio signal.
+
 It is a corpus of meeting-style conversations in French created for the purpose of the SUMM-RE project (ANR-20-CE23-0017). 
 
 The full dataset is described in Hunter et al. (2024): "SUMM-RE: A corpus of French meeting-style conversations".
@@ -77,11 +78,10 @@ The full dataset is described in Hunter et al. (2024): "SUMM-RE: A corpus of Fre
 
 ## Dataset Description
 
-Data from the `dev` and `test` splits have been manually transcribed and aligned and so are suitable for the evaluation of automatic speech recognition and voice activity detection models. 
+Data from the `dev` and `test` splits have been manually transcribed and aligned.
 
 Data from the `train` split has been automatically transcribed and aligned with the Whisper pipeline described in Yamasaki et al. (2023): "Transcribing And Aligning Conversational Speech: A Hybrid Pipeline Applied To French Conversations".
-
-The audio and transcripts used to evaluate this pipeline, a subset of the `dev` split<sup>*</sup>, can be found on [Ortolang](https://www.ortolang.fr/market/corpora/summ-re-asru/).
+The audio and transcripts used to evaluate this pipeline, a subset of the `dev` split<sup>(*)</sup>, can be found on [Ortolang](https://www.ortolang.fr/market/corpora/summ-re-asru/).
 
 The `dev` and `test` splits of SUMM-RE can be used for the evaluation of automatic speech recognition models and voice activity detection for conversational, spoken French.
 Speaker diarization can also be evaluated if several tracks of a same meeting are merged together.
@@ -295,7 +295,7 @@ Hiroyoshi Yamasaki, Jérôme Louradour, Julie Hunter and Laurent Prévot (2023):
   organization={IEEE}
 }
   ```
-<sup>*</sup>The following meetings were used to evaluate the pipeline in Yamasaki et al. (2023):
+<sup>(*)</sup>The following meetings were used to evaluate the pipeline in Yamasaki et al. (2023):
 
 ```python
 asru = ['018a_EARZ_055', '018a_EARZ_056', '018a_EARZ_057', '018a_EARZ_058', '020b_EBDZ_017', '020b_EBDZ_053', '020b_EBDZ_057', '020b_EBDZ_063', '027a_EBRH_025', '027a_EBRH_075', '027a_EBRH_078', '032b_EADH_084', '032b_EADH_085', '032b_EADH_086', '032b_EADH_087', '033a_EBRH_091', '033a_EBRH_092', '033a_EBRH_093', '033a_EBRH_094', '033c_EBPH_091', '033c_EBPH_092', '033c_EBPH_093', '033c_EBPH_094', '034a_EBRH_095', '034a_EBRH_096', '034a_EBRH_097', '034a_EBRH_098', '035b_EADH_088', '035b_EADH_096', '035b_EADH_097', '035b_EADH_098', '036c_EAPH_091', '036c_EAPH_092', '036c_EAPH_093', '036c_EAPH_099', '069c_EEPL_156', '069c_EEPL_157', '069c_EEPL_158', '069c_EEPL_159']