mlsanityData to 7 Oct 2026

VoiceShop: A Unified Speech-to-Speech Framework for Identity-Preserving Zero-Shot Voice Editing

arXiv 2404.06674 ↗First posted 10 Apr 2024cs.SD cs.AI eess.AScited by 7

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Abstract (arXiv)

We present VoiceShop, a novel speech-to-speech framework that can modify multiple attributes of speech, such as age, gender, accent, and speech style, in a single forward pass while preserving the input speaker's timbre. Previous works have been constrained to specialized models that can only edit these attributes individually and suffer from the following pitfalls: the magnitude of the conversion effect is weak, there is no zero-shot capability for out-of-distribution speakers, or the synthesized outputs exhibit undesirable timbre leakage. Our work proposes solutions for each of these issues in a simple modular framework based on a conditional diffusion backbone model with optional normalizing flow-based and sequence-to-sequence speaker attribute-editing modules, whose components can be combined or removed during inference to meet a wide array of tasks without additional model finetuning. Audio samples are available at urlhttps://voiceshopai.github.io.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 68 of its 84 references that are in the map. First posted on arXiv 10 Apr 2024.

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68 of 84 references.

References · 68

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38 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 2; 2026: 2.

7 papers.