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Towards Fine-Grained and Multi-Granular Contrastive Language-Speech Pre-training

Paper · 2026 · Yifan Yang, Bing Han, Hui Wang et al.

Summary

Speech–text contrastive models usually learn speaking style from coarse captions or narrow task labels. Existing style-caption corpora are typically built by first assigning discrete tags and then having an LLM rewrite them as prose, which loses nuance and spreads labeling mistakes. The authors address this with FCaps, an English corpus of about 47k hours of speech paired with 19M free-text style descriptions. Captions are produced end to end. A multimodal captioner listens to the audio, optionally using existing human tags, and proposes several candidates. A verification agent with tools such as ASR and regex matching then checks each candidate against a checklist. An LLM judge rates the resulting captions above cascaded ones for correctness, coverage and naturalness. On top of FCaps they train CLSP, a speech–text dual encoder that combines utterance-level (global) and temporally detailed (fine-grained) contrastive supervision. CLSP outperforms prior CLAP-style models on retrieval at both granularities, on zero-shot paralinguistic classification, and on style similarity scoring that tracks expert ratings. This makes it a low-cost alternative to audio-LLM judges, for example when evaluating instruction-following TTS.

Key points

  • FCaps covers 47.1k hours and 19M captions, which the authors describe as the largest fine-grained free-text speaking-style dataset. Its audio comes from Emilia, Expresso, EARS and VoxCeleb, organized into the subsets FCaps-Emilia and FCaps-PSCBase.
  • The paper distinguishes two caption types. Global captions give an atemporal speaker and style profile. Fine-grained captions describe how prosody, emphasis, style shifts and non-verbal sounds change within an utterance.
  • Zero-shot classification of emotion, gender and age was tested on IEMOCAP, RAVDESS and CREMA-D using WA/UA metrics. CLSP beat LAION-AI CLAP, GLAP, ParaCLAP and Auden-Voice CLAP across all three datasets.
  • For human alignment, 20 speech experts rated caption–audio match on the ParaSpeechCaps holdout split. CLSP correlated best with these ratings (Pearson, Spearman and Kendall) for intrinsic traits, situational traits and their fusion.
  • Captions are released under CC BY-NC-SA 4.0 without redistributing the audio, and the CLSP model under Apache 2.0, via GitHub and Hugging Face (yfyeung/FCaps, yfyeung/CLSP). The work currently covers English only.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Tsinghua UniversityBeijing · China415 works · 94,028 citations🎓Shanghai Jiao Tong UniversityShanghai · China212 works · 19,544 citations🏢TencentShenzhen · China163 works · 21,032 citations🎓Tianjin UniversityTianjin · China52 works · 1,128 citations🎓Shanghai Innovation InstituteShanghai · China34 works · 325 citations🎓Nankai UniversityTianjin · China36 works · 762 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksComputer Vision and Pattern Recognition · 3,486 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,894 works

Cite

Cite as Yifan Yang, Bing Han, Hui Wang, Wei Wang, Ziyang Ma, Long Zhou, Zengrui Jin, Guanrou Yang, Tianrui Wang, Xu Tan and Xie Chen. 2026. Towards Fine-Grained and Multi-Granular Contrastive Language-Speech Pre-training. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.194

@inproceedings{yang2026fine,
  title = {{Towards Fine-Grained and Multi-Granular Contrastive Language-Speech Pre-training}},
  author = {Yifan Yang, Bing Han, Hui Wang, Wei Wang, Ziyang Ma, Long Zhou, Zengrui Jin, Guanrou Yang, Tianrui Wang, Xu Tan and Xie Chen},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.194},
  url = {https://doi.org/10.18653/v1/2026.acl-long.194}
}

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