Datasets:

abnerh
/

TORGO-database

transcription stringclasses 967 values	speech_status stringclasses 2 values	gender stringclasses 2 values	duration float64 0.15 194
alpha	healthy	female	3.3
the	healthy	female	3.45
Except in the winter when the ooze or snow or ice prevents	healthy	female	7.2
raid	healthy	female	3.6
read	healthy	female	3.45
stubble	healthy	female	4.5
ate	healthy	female	2.4
store	healthy	female	3.6
sip	healthy	female	3
wish	healthy	female	3.9
slay	healthy	female	3.3
sigh	healthy	female	3.3
jagged	healthy	female	3.45
up	healthy	female	3
chair	healthy	female	3.6
one	healthy	female	3.15
Don't ask me to carry an oily rag like that	healthy	female	5.25
rock	healthy	female	3.3
double	healthy	female	3.45
form	healthy	female	3.3
We have often urged him to walk more and smoke less	healthy	female	6.75
warm	healthy	female	3.3
white	healthy	female	3.3
giving those who observe him a pronounced feeling of the utmost respect	healthy	female	6.45
car	healthy	female	3.45
When he speaks his voice is just a bit cracked and quivers a trifle	healthy	female	6
bubble	healthy	female	3.3
born	healthy	female	3.6
troop	healthy	female	3
play	healthy	female	3.3
Nothing is as offensive as innocence	healthy	female	4.8
goat	healthy	female	3
slip	healthy	female	3
whoop	healthy	female	3.75
sticks	healthy	female	3.6
gadget	healthy	female	3.15
pat	healthy	female	2.55
air	healthy	female	3.9
two	healthy	female	2.85
storm	healthy	female	3.9
hear	healthy	female	3.3
fair	healthy	female	3.45
corn	healthy	female	3.45
galore	healthy	female	3.75
rocks	healthy	female	3
dark	healthy	female	2.7
range	healthy	female	3.6
farm	healthy	female	3
feed	healthy	female	3.45
air	healthy	female	3.3
urgent	healthy	female	3.15
grow	healthy	female	3.3
but he always answers Banana oil	healthy	female	5.4
tip	healthy	female	3.15
rake	healthy	female	3.6
tear	healthy	female	3.45
ate	healthy	female	3.3
steer	healthy	female	3.6
I just try to do my best	healthy	female	4.05
left	healthy	female	3.6
We gathered shells on the beach	healthy	female	4.65
knew	healthy	female	3.45
for	healthy	female	3.6
write	healthy	female	3.45
Well he is nearly ninetythree years old	healthy	female	5.7
bat	healthy	female	3.6
horn	healthy	female	2.85
rate	healthy	female	3.3
boot	healthy	female	3.15
usually minus several buttons	healthy	female	4.35
stick	healthy	female	4.05
he slowly takes a short walk in the open air each day	healthy	female	5.7
knew	healthy	female	3.3
You wished to know all about my grandfather	healthy	female	5.7
suit	healthy	female	2.55
spark	healthy	female	3.3
beta	healthy	female	3.75
swore	healthy	female	3.6
swarm	healthy	female	3.15
Twice each day he plays skillfully and with zest upon our small organ	healthy	female	7.35
dug	healthy	female	2.25
knee	healthy	female	3.3
but he always answers Banana oil	healthy	female	3.75
race	healthy	female	3.3
beat	healthy	female	3
swarm	healthy	female	3.45
storm	healthy	female	3.9
meat	healthy	female	3.6
sleep	healthy	female	3.15
ship	healthy	female	3
prior	healthy	female	3.3
sip	healthy	female	3
usually minus several buttons	healthy	female	4.2
deer	healthy	female	3
jacket	healthy	female	3.3
rain	healthy	female	3
fee	healthy	female	3.45
feet	healthy	female	3.6
trace	healthy	female	3.6
down	healthy	female	3.45

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The TORGO Database: Acoustic and articulatory speech from speakers with dysarthria

Dataset Summary

This database only includes the short words and restricted sentence portion of the TORGO dataset.
For the full dataset which also includes non-words and unrestricted sentences please see: https://www.cs.toronto.edu/~complingweb/data/TORGO/torgo.html.
Transcripts have been normalized to remove punctuation but casing has been left. Few transcripts only had 'xxx' as text, these were removed.
About 5.5 hours of dysarthric speech data and 8 hours of healthy speech.

Short words
These are useful for studying speech acoustics without the need for word boundary detection. This category includes the following:

Repetitions of the English digits, 'yes', 'no', 'up', 'down', 'left', 'right', 'forward', 'back', 'select', 'menu', and the international radio alphabet (e.g., 'alpha', 'bravo', 'charlie'). These words are useful for hypothetical command software for accessibility.
50 words from the the word intelligibility section of the Frenchay Dysarthria Assessment (Enderby, 1983).
360 words from the word intelligibility section of the Yorkston-Beukelman Assessment of Intelligibility of Dysarthric Speech (Yorkston and Beukelman, 1981).
The 10 most common words in the British National Corpus.

Restricted sentences
In order to utilize lexical, syntactic, and semantic processing in ASR, full and syntactically correct sentences are recorded. These include the following:

Preselected phoneme-rich sentences such as "The quick brown fox jumps over the lazy dog", "She had your dark suit in greasy wash water all year", and "Don't ask me to carry an oily rag like that."
The Grandfather passage.
162 sentences from the sentence intelligibility section of the Yorkston-Beukelman Assessment of Intelligibility of Dysarthric Speech (Yorkston and Beukelman, 1981).
The 460 TIMIT-derived sentences used as prompts in the MOCHA-TIMIT database (Wrench, 1999; Zue et al, 1989).

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
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)

from datasets import load_dataset

dataset = load_dataset("abnerh/TORGO-database")
print(dataset) 
DatasetDict({
    train: Dataset({
        features: ['audio', 'transcription', 'speech_status', 'gender', 'duration'],
        num_rows: 16552
    })
})

dataset = load_dataset("abnerh/TORGO-database")
print(dataset['train'][0]) 
{'audio': {'path': 'FC01_1_arrayMic_0066.wav',
  'array': array([ 0.00125122,  0.00387573,  0.00115967, ...,  0.00149536,
         -0.00326538,  0.00027466]),
  'sampling_rate': 16000},
 'transcription': 'alpha',
 'speech_status': 'healthy',
 'gender': 'female',
 'duration': 3.3}

print(dataset['train'][12200]) 
{'audio': {'path': 'M02_1_headMic_0066.wav',
  'array': array([ 0.00115967,  0.00106812,  0.00091553, ..., -0.00073242,
         -0.00082397, -0.00054932]),
  'sampling_rate': 16000},
 'transcription': 'yet he still thinks as swiftly as ever',
 'speech_status': 'dysarthria',
 'gender': 'male',
 'duration': 7.605}

Use of this database is free for academic (non-profit) purposes. 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. This may be the most informative of the database itself.
Rudzicz, F.(2012) Using articulatory likelihoods in the recognition of dysarthric speech. Speech Communication, 54(3), March, pages 430--444.