ProPS: Prompted Profile Synthesis for Natural Language-Conditioned Speaker Embedding Distributions
arXiv:2607.05276v1 Announce Type: cross Abstract: Speaker embeddings, or x-vectors, are widely used to represent speaker identity and speaker-related attributes, but existing embedding extractors are typically descriptive rather than generative: they map an observed speech segment to an x-vector, which is then used for downstream applications. We introduce ProPS, Prompted Profile Synthesis, a framework for generating distributions of speaker embeddings conditioned on natural language prompts such as "a thirties male speaker with an Indian accent". ProPS converts human-written profile descripti
The proliferation of advanced AI models necessitates more granular and controllable methods for synthesising nuanced data representations, moving beyond purely descriptive approaches.
This development allows for programmatic generation of speaker profiles from natural language, enabling more sophisticated and ethically sensitive applications in areas like synthetic media and voice AI.
The ability to generate speaker embedding distributions from natural language prompts shifts speaker identity representation from purely analytical to also generative and controllable.
- · AI developers
- · Synthetic media platforms
- · Voice assistants
- · Audio content creation
- · Platforms without robust identity controls
- · Low-fidelity voice synthesis methods
More realistic and diverse synthetic voice outputs become programmatically accessible.
New applications emerge in content creation, accessibility, and communication, leveraging highly specific voice profiles.
Enhanced impersonation risks and deeper challenges for voice-based authentication systems could materialise if not properly safeguarded.
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Read at arXiv cs.AI