ProPS: Prompted Profile Synthesis for Natural Language-Conditioned Speaker Embedding Distributions
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 descriptions into sentence embeddings and uses a mixture de
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
- Linked via arxiv authorThomas Thebaud →
ProPS: Prompted Profile Synthesis for Natural Language-Conditioned Speaker Embedding Distributions
- Linked via arxiv authorJunhyeok Lee →
ProPS: Prompted Profile Synthesis for Natural Language-Conditioned Speaker Embedding Distributions
- Linked via arxiv authorLaureano Moro-Velazquez →
ProPS: Prompted Profile Synthesis for Natural Language-Conditioned Speaker Embedding Distributions
- Linked via arxiv authorJesus Villalba Lopez →
ProPS: Prompted Profile Synthesis for Natural Language-Conditioned Speaker Embedding Distributions
- Linked via arxiv authorNajim Dehak →
ProPS: Prompted Profile Synthesis for Natural Language-Conditioned Speaker Embedding Distributions