Speech After Gender: A Trans-Feminine Perspective on Next Steps for Speech Science and Technology
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866909249486979072 |
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| author | Netzorg, Robin Cote, Alyssa Koshin, Sumi Garoute, Klo Vivienne Anumanchipalli, Gopala Krishna |
| author_facet | Netzorg, Robin Cote, Alyssa Koshin, Sumi Garoute, Klo Vivienne Anumanchipalli, Gopala Krishna |
| contents | As experts in voice modification, trans-feminine gender-affirming voice teachers have unique perspectives on voice that confound current understandings of speaker identity. To demonstrate this, we present the Versatile Voice Dataset (VVD), a collection of three speakers modifying their voices along gendered axes. The VVD illustrates that current approaches in speaker modeling, based on categorical notions of gender and a static understanding of vocal texture, fail to account for the flexibility of the vocal tract. Utilizing publicly-available speaker embeddings, we demonstrate that gender classification systems are highly sensitive to voice modification, and speaker verification systems fail to identify voices as coming from the same speaker as voice modification becomes more drastic. As one path towards moving beyond categorical and static notions of speaker identity, we propose modeling individual qualities of vocal texture such as pitch, resonance, and weight. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_07235 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Speech After Gender: A Trans-Feminine Perspective on Next Steps for Speech Science and Technology Netzorg, Robin Cote, Alyssa Koshin, Sumi Garoute, Klo Vivienne Anumanchipalli, Gopala Krishna Sound Machine Learning Audio and Speech Processing As experts in voice modification, trans-feminine gender-affirming voice teachers have unique perspectives on voice that confound current understandings of speaker identity. To demonstrate this, we present the Versatile Voice Dataset (VVD), a collection of three speakers modifying their voices along gendered axes. The VVD illustrates that current approaches in speaker modeling, based on categorical notions of gender and a static understanding of vocal texture, fail to account for the flexibility of the vocal tract. Utilizing publicly-available speaker embeddings, we demonstrate that gender classification systems are highly sensitive to voice modification, and speaker verification systems fail to identify voices as coming from the same speaker as voice modification becomes more drastic. As one path towards moving beyond categorical and static notions of speaker identity, we propose modeling individual qualities of vocal texture such as pitch, resonance, and weight. |
| title | Speech After Gender: A Trans-Feminine Perspective on Next Steps for Speech Science and Technology |
| topic | Sound Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2407.07235 |