Unmixing the Crowd: Learning Mixture-to-Set Speaker Embeddings for Enrollment-Free Target Speech Extraction
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866910101047083008 |
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| author | Sidharth, FNU Asgari, Meysam Dong, Hao-Wen Jain, Dhruv |
| author_facet | Sidharth, FNU Asgari, Meysam Dong, Hao-Wen Jain, Dhruv |
| contents | Personalized or target speech extraction (TSE) typically needs a clean enrollment -- hard to obtain in real-world crowded environments. We remove the essential need for enrollment by predicting, from the mixture itself, a small set of per-speaker embeddings that serve as the control signal for extraction. Our model maps a noisy mixture directly to a small set of candidate speaker embeddings trained to align with a strong single-speaker speaker-embedding space via permutation-invariant teacher supervision. On noisy LibriMix, the resulting embeddings form a structured and clusterable identity space, outperforming WavLM+K-means and separation-derived embeddings in standard clustering metrics. Conditioning these embeddings into multiple extraction back-ends consistently improves objective quality and intelligibility, and generalizes to real DNS-Challenge recordings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_03219 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Unmixing the Crowd: Learning Mixture-to-Set Speaker Embeddings for Enrollment-Free Target Speech Extraction Sidharth, FNU Asgari, Meysam Dong, Hao-Wen Jain, Dhruv Audio and Speech Processing Sound Personalized or target speech extraction (TSE) typically needs a clean enrollment -- hard to obtain in real-world crowded environments. We remove the essential need for enrollment by predicting, from the mixture itself, a small set of per-speaker embeddings that serve as the control signal for extraction. Our model maps a noisy mixture directly to a small set of candidate speaker embeddings trained to align with a strong single-speaker speaker-embedding space via permutation-invariant teacher supervision. On noisy LibriMix, the resulting embeddings form a structured and clusterable identity space, outperforming WavLM+K-means and separation-derived embeddings in standard clustering metrics. Conditioning these embeddings into multiple extraction back-ends consistently improves objective quality and intelligibility, and generalizes to real DNS-Challenge recordings. |
| title | Unmixing the Crowd: Learning Mixture-to-Set Speaker Embeddings for Enrollment-Free Target Speech Extraction |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2604.03219 |