Analyzing and Improving Speaker Similarity Assessment for Speech Synthesis
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Abstract (arXiv)
Modeling voice identity is challenging due to its multifaceted nature. In generative speech systems, identity is often assessed using automatic speaker verification (ASV) embeddings, designed for discrimination rather than characterizing identity. This paper investigates which aspects of a voice are captured in such representations. We find that widely used ASV embeddings focus mainly on static features like timbre and pitch range, while neglecting dynamic elements such as rhythm. We also identify confounding factors that compromise speaker similarity measurements and suggest mitigation strategies. To address these gaps, we propose U3D, a metric that evaluates speakers' dynamic rhythm patterns. This work contributes to the ongoing challenge of assessing speaker identity consistency in the context of ever-better voice cloning systems. We publicly release our code.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 32 of its 62 references that are in the map. First posted on arXiv 2 Jul 2025.
Builds on
32 of 62 references.
References · 32
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- 2024ConsistentID: Portrait Generation with Multimodal Fine-Grained Identity Preservingcited by 42
- 2021Investigating on Incorporating Pretrained and Learnable Speaker Representations for Multi-Speaker Multi-Style Text-to-Speechcited by 37
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- 2024The VoxCeleb Speaker Recognition Challenge: A Retrospectivecited by 9
- 2024Evaluating Text-to-Speech Synthesis from a Large Discrete Token-based Speech Language Modelcited by 8
- 2021The Multi-speaker Multi-style Voice Cloning Challenge 2021cited by 7
- 2024Rethinking MUSHRA: Addressing Modern Challenges in Text-to-Speech Evaluationcited by 7
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- 2024VoxGenesis: Unsupervised Discovery of Latent Speaker Manifold for Speech Synthesiscited by 4
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- 2022Automatic Evaluation of Speaker Similaritycited by 2
- 2024ASRRL-TTS: Agile Speaker Representation Reinforcement Learning for Text-to-Speech Speaker Adaptationcited by 1
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Citing papers per year: 2026: 3.
3 papers.