Add speaker-level Frenchay severity labels

Add a severity field to every example. Dysarthric speakers are labeled severe, moderate-severe, moderate, or mild from the published Frenchay categories. Control speakers are labeled typical.

README.md

@@ -11,11 +11,13 @@ dataset_info:
     dtype: string
   - name: duration
     dtype: float64
+  - name: severity
+    dtype: string
   splits:
   - name: train
     num_bytes: 1741001462.72
     num_examples: 16552
-  download_size: 1564871166
+  download_size: 1563788234
   dataset_size: 1741001462.72
 configs:
 - config_name: default
@@ -61,12 +63,23 @@ In order to utilize lexical, syntactic, and semantic processing in ASR, full and
 
 ## Dataset Structure
 - Data points comprise the path to the audio file and its transcription.
-- Additional fields include gender, speech status (dysarthria or healthy), and duration
+- Additional fields include gender, speech status (dysarthria or healthy), duration, and speaker-level severity.
 - No dev/test split is provided as there is no standard split for this dataset.
 - Filenames are as follows:
   - speakerNumber_sessionNumber_micType_utteranceNumber.wav
     - Speaker number has the format of gender-speechStatus-speakerNumber (e.g. FC01 = Female control #1, M04 = Male dysarthric #4)
 
+## Severity
+`severity` is a speaker-level clinical category from the Frenchay Dysarthria Assessment, not an utterance-level intelligibility score. Every row from a speaker has the same value. Dysarthric speakers follow Mengistu and Rudzicz (ICASSP 2011) and Joy and Umesh (Interspeech 2017). Control speakers were not assessed and are labeled `typical`.
+
+| Speakers | severity |
+| --- | --- |
+| F01, M01, M02, M04 | severe |
+| M05 | moderate-severe |
+| F03 | moderate |
+| F04, M03 | mild |
+| FC01, FC02, FC03, MC01, MC02, MC03, MC04 | typical |
+
 
 
 ```python
@@ -76,7 +89,7 @@ dataset = load_dataset("abnerh/TORGO-database")
 print(dataset) 
 DatasetDict({
     train: Dataset({
-        features: ['audio', 'transcription', 'speech_status', 'gender', 'duration'],
+        features: ['audio', 'transcription', 'speech_status', 'gender', 'duration', 'severity'],
         num_rows: 16552
     })
 })
@@ -92,7 +105,8 @@ print(dataset['train'][0])
  'transcription': 'alpha',
  'speech_status': 'healthy',
  'gender': 'female',
- 'duration': 3.3}
+ 'duration': 3.3,
+ 'severity': 'typical'}
 ```
 ```python
 print(dataset['train'][12200]) 
@@ -103,11 +117,16 @@ print(dataset['train'][12200])
  'transcription': 'yet he still thinks as swiftly as ever',
  'speech_status': 'dysarthria',
  'gender': 'male',
- 'duration': 7.605}
+ 'duration': 7.605,
+ 'severity': 'severe'}
 ```
 
 
 <b>Use of this database is free for academic (non-profit) purposes.</b> If you use these data in any publication, you must reference at least one of the following papers:
 - Rudzicz, F., Hirst, G., Van Lieshout, P. (2012) Vocal tract representation in the recognition of cerebral palsied speech. The Journal of Speech, Language, and Hearing Research, 55(4):1190-1207, August.
 - Rudzicz, F., Namasivayam, A.K., Wolff, T. (2012) The TORGO database of acoustic and articulatory speech from speakers with dysarthria. Language Resources and Evaluation, 46(4), pages 523--541. <b>This may be the most informative of the database itself.</b>
-- Rudzicz, F.(2012) Using articulatory likelihoods in the recognition of dysarthric speech. Speech Communication, 54(3), March, pages 430--444.
+- Rudzicz, F.(2012) Using articulatory likelihoods in the recognition of dysarthric speech. Speech Communication, 54(3), March, pages 430--444.
+
+The severity labels are taken from:
+- Mengistu, K. T. and Rudzicz, F. (2011) Adapting acoustic and lexical models to dysarthric speech. Proc. ICASSP 2011.
+- Joy, N. M. and Umesh, S. (2017) On improving acoustic models for TORGO dysarthric speech database. Proc. Interspeech 2017.

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