SoloSpeech: Enhancing Intelligibility and Quality in Target Speech Extraction through a Cascaded Generative Pipeline

Helin Wang1, Jiarui Hai1, Dongchao Yang2, Chen Chen3, Kai Li4, Junyi Peng5, Thomas Thebaud1, Laureano Moro Velazquez1, Jesus Villalba1, Najim Dehak1
1Johns Hopkins University 2The Chinese University of Hong Kong 3Nanyang Technological University 4Tsinghua University 5Brno University of Technology
Paper Code Demo

Target Speech Extraction (TSE) aims to isolate a target speaker’s voice from a mixture of multiple speakers by leveraging speaker-specific cues. 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. In contrast, 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 state-of-the-art intelligibility and quality in target speech extraction and speech separation tasks while demonstrating exceptional generalization on out-of-domain data, providing at least a 16.1% SISNR improvement over the previous best method, USEF-TSE. These findings underscore SoloSpeech’s robustness and versatility in diverse acoustic environments.

Overall Pipeline

Overall Pipeline of SoloSpeech.

Table of Contents

Comparison on Real-recorded Datasets

CHiME-5

Mixture Cue SoloSpeech USEF-TSE SoloAudio

RealSEP

Mixture Cue SoloSpeech USEF-TSE SoloAudio
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Comparison with SOTAs on the Libri2Mix

Mixture Cue Target SoloSpeech USEF-TSE SoloAudio
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Abalation Studies for the Compressor on the Libri2Mix

Mixture Cue Target T-F Audio VAE Stable Audio VAE
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Abalation Studies for the Corrector on the Libri2Mix

Mixture Cue Target w/ Corrector w/o Corrector
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BibTeX

@misc{wang2025solospeechenhancingintelligibilityquality,
      title={SoloSpeech: Enhancing Intelligibility and Quality in Target Speech Extraction through a Cascaded Generative Pipeline}, 
      author={Helin Wang and Jiarui Hai and Dongchao Yang and Chen Chen and Kai Li and Junyi Peng and Thomas Thebaud and Laureano Moro Velazquez and Jesus Villalba and Najim Dehak},
      year={2025},
      eprint={2505.19314},
      archivePrefix={arXiv},
      primaryClass={eess.AS},
      url={https://arxiv.org/abs/2505.19314}, 
}