title: SoloSpeech: Enhancing Intelligibility and Quality in Target Speech
    Extraction through a Cascaded Generative Pipeline
summary: Target Speech Extraction (TSE) aims to isolate a target speaker's voice from
  a mixture of multiple speakers by leveraging speaker-specific cues, typically
  provided as auxiliary audio (a.k.a. cue audio). Although recent advancements in
  TSE have primarily employed discriminative models that offer high perceptual
  quality, these models often introduce unwanted artifacts, reduce naturalness,
  and are sensitive to discrepancies between training and testing environments.
  On the other hand, generative models for TSE lag in perceptual quality and
  intelligibility. To address these challenges, we present SoloSpeech, a novel
  cascaded generative pipeline that integrates compression, extraction,
  reconstruction, and correction processes. SoloSpeech features a
  speaker-embedding-free target extractor that utilizes conditional information
  from the cue audio's latent space, aligning it with the mixture audio's latent
  space to prevent mismatches. Evaluated on the widely-used Libri2Mix dataset,
  SoloSpeech achieves the new state-of-the-art intelligibility and quality in
  target speech extraction and speech separation tasks while demonstrating
  exceptional generalization on out-of-domain data and real-world scenarios.
paper_authors._id: 683675205b96c192536256d1
paper_authors.name: Helin Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-28T12:12:13.955Z
paper_authors._id: 63ecfb5ec5b3c734085db9ed
paper_authors.avatarUrl: /avatars/0b1d03dcd7997ad1daa764fb76f88993.svg
paper_authors.fullname: Helin Wang
paper_authors.type: user
paper_authors.user: westbrook
paper_authors._id: 683675205b96c192536256d2
paper_authors.name: Jiarui Hai
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-06T07:42:22.785Z
paper_authors._id: 6363e9e4969bdae89e10b5ac
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6363e9e4969bdae89e10b5ac/uR0jz8pBJIQPmKC90OJgI.jpeg
paper_authors.fullname: Jiarui Hai
paper_authors.type: user
paper_authors.user: Higobeatz
paper_authors._id: 683675205b96c192536256d3
paper_authors.name: Dongchao Yang
paper_authors._id: 683675205b96c192536256d4
paper_authors.name: Chen Chen
paper_authors._id: 683675205b96c192536256d5
paper_authors.name: Kai Li
paper_authors._id: 683675205b96c192536256d6
paper_authors.name: Junyi Peng
paper_authors._id: 683675205b96c192536256d7
paper_authors.name: Thomas Thebaud
paper_authors._id: 683675205b96c192536256d8
paper_authors.name: Laureano Moro Velazquez
paper_authors._id: 683675205b96c192536256d9
paper_authors.name: Jesus Villalba
paper_authors._id: 683675205b96c192536256da
paper_authors.name: Najim Dehak
paper_title: SoloSpeech: Enhancing Intelligibility and Quality in Target Speech
    Extraction through a Cascaded Generative Pipeline
paper_summary: Target Speech Extraction (TSE) aims to isolate a target speaker's voice from
  a mixture of multiple speakers by leveraging speaker-specific cues, typically
  provided as auxiliary audio (a.k.a. cue audio). Although recent advancements in
  TSE have primarily employed discriminative models that offer high perceptual
  quality, these models often introduce unwanted artifacts, reduce naturalness,
  and are sensitive to discrepancies between training and testing environments.
  On the other hand, generative models for TSE lag in perceptual quality and
  intelligibility. To address these challenges, we present SoloSpeech, a novel
  cascaded generative pipeline that integrates compression, extraction,
  reconstruction, and correction processes. SoloSpeech features a
  speaker-embedding-free target extractor that utilizes conditional information
  from the cue audio's latent space, aligning it with the mixture audio's latent
  space to prevent mismatches. Evaluated on the widely-used Libri2Mix dataset,
  SoloSpeech achieves the new state-of-the-art intelligibility and quality in
  target speech extraction and speech separation tasks while demonstrating
  exceptional generalization on out-of-domain data and real-world scenarios.
paper_ai_summary: SoloSpeech, a cascaded generative pipeline, improves target speech extraction and speech separation by addressing artifact introduction, naturalness reduction, and environment mismatches, achieving state-of-the-art intelligibility and quality.
paper_ai_keywords: target speech extraction discriminative models generative models speaker-embedding-free target extractor latent space Libri2Mix dataset speech separation