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pro vyhledávání: '"Minixhofer, Christoph"'
Synthetically generated speech has rapidly approached human levels of naturalness. However, the paradox remains that ASR systems, when trained on TTS output that is judged as natural by humans, continue to perform badly on real speech. In this work,
Externí odkaz:
http://arxiv.org/abs/2410.12279
Many recently published Text-to-Speech (TTS) systems produce audio close to real speech. However, TTS evaluation needs to be revisited to make sense of the results obtained with the new architectures, approaches and datasets. We propose evaluating th
Externí odkaz:
http://arxiv.org/abs/2407.12707
While modern Text-to-Speech (TTS) systems can produce natural-sounding speech, they remain unable to reproduce the full diversity found in natural speech data. We consider the distribution of all possible real speech samples that could be generated b
Externí odkaz:
http://arxiv.org/abs/2211.16049
In this work, we unify several existing decoding strategies for punctuation prediction in one framework and introduce a novel strategy which utilises multiple predictions at each word across different windows. We show that significant improvements ca
Externí odkaz:
http://arxiv.org/abs/2112.08098
Publikováno v:
Minixhofer, C, Swan, M, McMeekin, C & Andreadis, P 2021, ' DroughtED: A dataset and methodology for drought forecasting spanning multiple climate zones ', Paper presented at Tackling Climate Change with Machine Learning, 23/07/21-23/07/21 .
Climate change exacerbates the frequency, duration and extent of extreme weather events such as drought. Previous attempts to forecast drought conditions using machine learning have focused on regional models which have two major limitations for nati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3094::472612b36a02050b147382eefd27def7
https://www.pure.ed.ac.uk/ws/files/217133242/DroughtED_MINIXHOFER_DOA18062021_AFV.pdf
https://www.pure.ed.ac.uk/ws/files/217133242/DroughtED_MINIXHOFER_DOA18062021_AFV.pdf