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README.md
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license: cc-by-4.0
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This corpus contains paired data of speech, articulatory movements and phonemes. There are 38 speakers in the corpus, each with 460 utterances.
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The raw audio files are in audios.zip. The ema data and preprocessed data is stored in processed.zip. The processed data can be loaded with pytorch and has the following keys -
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<ul>
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<li>ema_raw : The raw ema data
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<li>ema_clipped : The ema data after trimming using being-end time stamps
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<li>ema_trimmed_and_normalised_with_6_articulators: The ema data after trimming using being-end time stamps, followed by articulatory specifc standardisation
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<li>mfcc: 13-dim MFCC computed on trimmed audio
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<li>phonemes: The phonemes uttered for the audio
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<li>durations: Duration values for each phoneme
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<li>begin_end: Begin end time stamps to trim the audio / raw ema
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</ul>
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To use this data for tasks such as acoustic to articulatory inversion (AAI), you can use ema_trimmed_and_normalised_with_6_articulators and mfcc as the data.
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---
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license: cc-by-4.0
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---
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This corpus contains paired data of speech, articulatory movements and phonemes. There are 38 speakers in the corpus, each with 460 utterances.
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The raw audio files are in audios.zip. The ema data and preprocessed data is stored in processed.zip. The processed data can be loaded with pytorch and has the following keys -
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<ul>
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<li>ema_raw : The raw ema data
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+
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<li>ema_clipped : The ema data after trimming using being-end time stamps
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+
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<li>ema_trimmed_and_normalised_with_6_articulators: The ema data after trimming using being-end time stamps, followed by articulatory specifc standardisation
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<li>mfcc: 13-dim MFCC computed on trimmed audio
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<li>phonemes: The phonemes uttered for the audio
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<li>durations: Duration values for each phoneme
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<li>begin_end: Begin end time stamps to trim the audio / raw ema
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</ul>
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To use this data for tasks such as acoustic to articulatory inversion (AAI), you can use ema_trimmed_and_normalised_with_6_articulators and mfcc as the data.
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___
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If you have used this dataset in your work, use the following refrence to cite the dataset -
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```Bandekar, J., Udupa, S., Ghosh, P.K. (2024) Articulatory synthesis using representations learnt through phonetic label-aware contrastive loss. Proc. Interspeech 2024, 427-431, doi: 10.21437/Interspeech.2024-1756```
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