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@@ -44,7 +44,9 @@ predictively synthesise paralanguage from text without such components, we provi
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  different levels of information removal (removal of non-speech events, removal of non-sentence elements,
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  and removal of false starts).
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- Benchmark TTS models for each transcript can be found here.
 
 
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  # Dataset Details
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@@ -60,4 +62,16 @@ The training set contains 90% of the data, the validation set contains 5% of the
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  # Citation
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- If you use this dataset, please cite the paper in which it is presented: _Kyra Wang, Dorien Herremans, 2024, DisfluencySpeech - Single-Speaker Conversational Speech Dataset with Paralanguage._
 
 
 
 
 
 
 
 
 
 
 
 
 
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  different levels of information removal (removal of non-speech events, removal of non-sentence elements,
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  and removal of false starts).
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+ Read the paper [here](https://arxiv.org/abs/2406.08820).
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+
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+ Benchmark TTS models for each transcript can be found here: [Transcript A](https://huggingface.co/amaai-lab/DisfluencySpeech_BenchmarkA), [Transcript B](https://huggingface.co/amaai-lab/DisfluencySpeech_BenchmarkB), and [Transcript C](https://huggingface.co/amaai-lab/DisfluencySpeech_BenchmarkC).
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  # Dataset Details
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  # Citation
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+ If you use this dataset, please cite the paper in which it is presented:
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+
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+ ```
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+ @misc{wang2024disfluencyspeechsinglespeakerconversational,
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+ title={DisfluencySpeech -- Single-Speaker Conversational Speech Dataset with Paralanguage},
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+ author={Kyra Wang and Dorien Herremans},
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+ year={2024},
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+ eprint={2406.08820},
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+ archivePrefix={arXiv},
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+ primaryClass={eess.AS},
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+ url={https://arxiv.org/abs/2406.08820},
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+ }
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+ ```