Speech After Gender: A Trans-Feminine Perspective on Next Steps for Speech Science and Technology
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 13 of its 31 references that are in the map. First posted on arXiv 9 Jul 2024.
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13 of 31 references.
References · 13
- 2017VoxCeleb: a large-scale speaker identification datasetcited by 714
- 2020ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verificationcited by 606
- 2024NaturalSpeech 3: Zero-Shot Speech Synthesis with Factorized Codec and Diffusion Modelscited by 222
- 2023Audiobox: Unified Audio Generation with Natural Language Promptscited by 120
- 2022PromptTTS: Controllable Text-to-Speech with Text Descriptionscited by 106
- 2021ECAPA-TDNN Embeddings for Speaker Diarizationcited by 47
- 2019Speaker Anonymization Using X-vector and Neural Waveform Modelscited by 41
- 2022Robust Disentangled Variational Speech Representation Learning for Zero-shot Voice Conversioncited by 28
- 2019Margin Matters: Towards More Discriminative Deep Neural Network Embeddings for Speaker Recognitioncited by 26
- 2023Speaker anonymization using neural audio codec language modelscited by 21
- 2022A Review of Speech-centric Trustworthy Machine Learning: Privacy, Safety, and Fairnesscited by 13
- 2022Reducing Geographic Disparities in Automatic Speech Recognition via Elastic Weight Consolidationcited by 3
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