Analysis by synthesis: Using an expressive TTS Model as feature extractor for 
paralinguistic speech classification

Schiller, Dominik; Mertes, Silvan; van Rijn, Pol; André, Elisabeth

doi:10.21437/Interspeech.2021-1587

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Analysis by synthesis: Using an expressive TTS Model as feature extractor for paralinguistic speech classification

Schiller, D., Mertes, S., van Rijn, P., & André, E. (2021). Analysis by synthesis: Using an expressive TTS Model as feature extractor for paralinguistic speech classification. In Proceedings Interspeech 2021 (pp. 486-490). doi:10.21437/Interspeech.2021-1587.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000B-46B9-6 Version Permalink: https://hdl.handle.net/21.11116/0000-000B-46BA-5

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Creators:
Schiller, Dominik¹, Author
Mertes, Silvan¹, Author
van Rijn, Pol², Author
André, Elisabeth¹, Author

Affiliations:
1Human-Centered Artificial Intelligence , Augsburg, Germany, ou_persistent22
2Department of Neuroscience, Max Planck Institute for Empirical Aesthetics, Max Planck Society, ou_2421697

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Language(s): eng - English

Dates: Published Online: 2021

Publication Status: Published online

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Identifiers: DOI: 10.21437/Interspeech.2021-1587

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Title: Interspeech 2021

Place of Event: Brno, Czechia

Start-/End Date: 2021-08-30 - 2021-09-03

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Title: Proceedings Interspeech 2021

Source Genre: Proceedings

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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 486 - 490 Identifier: -