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Assessing speaker-independent character information for acted voices

Abstract : While the natural voice is spontaneously generated by people, the acted voice is a controlled vocal interpretation, produced by professional actors and aimed at creating a desired effect on the listener. In this work, we pay attention to the aspects of the voice related to the character played. We particularly focus on actors playing the same video game role in different languages. This article is based on a recent work which proposes to build a neural-network-based voice representation dedicated to the character aspects, namely p-vector. This representation is learnt from recordings only labeled with the acted character. It showed its ability to associate two vocal examples related to the same character, even if the character is unknown during the training phase. However, there is still a possible confusion between speaker and character dimension. To tackle this problem, We propose a protocol to highlight the speaker-independent part of the character information (SICI). We compare the original voice representation with an alternative where the information relating to the characters is neutralised. This experiment shows that performance is not a sufficient metric to assess the quality of a character representation. It also offers the first evidence of the SICI in the voice.
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Contributor : Mathias Quillot Connect in order to contact the contributor
Submitted on : Sunday, September 19, 2021 - 2:19:31 PM
Last modification on : Thursday, December 1, 2022 - 11:26:04 AM
Long-term archiving on: : Tuesday, December 21, 2021 - 9:07:59 AM


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  • HAL Id : hal-03348572, version 1


Mathias Quillot, Richard Dufour, Jean-François Bonastre. Assessing speaker-independent character information for acted voices. 23rd International Conference on Speech and Computer (SPECOM), Sep 2021, Saint Petersburg, Russia. ⟨hal-03348572⟩



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