---
title: "LLM-ForcedAligner — paper by Mu et al. (2026)"
description: "The authors propose LLM-ForcedAligner, which recasts FA as slot filling. 31 references, cited by 1 paper here."
authors: ["Bingshen Mu", "Xian Shi", "Xiong Wang", "Hexin Liu", "Jin Xu", "Lei Xie"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.1174/"
doi: "10.18653/v1/2026.acl-long.1174"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/mu-2026-llm-forcedaligner-non-autoregressive-accurate/"
---
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# LLM-ForcedAligner: A Non-Autoregressive and Accurate LLM-Based Forced Aligner for Multilingual and Long-Form Speech

**Authors:** [Bingshen Mu](https://md.research.elamur.ai/free/en/authors/bingshen-mu/), [Xian Shi](https://md.research.elamur.ai/free/en/authors/xian-shi/), [Xiong Wang](https://md.research.elamur.ai/free/en/authors/xiong-wang/), [Hexin Liu](https://md.research.elamur.ai/free/en/authors/hexin-liu/), [Jin Xu](https://md.research.elamur.ai/free/en/authors/jin-xu/) and [Lei Xie](https://md.research.elamur.ai/free/en/authors/lei-xie/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.1174/) · [PDF](https://aclanthology.org/2026.acl-long.1174.pdf) · [DOI 10.18653/v1/2026.acl-long.1174](https://doi.org/10.18653/v1/2026.acl-long.1174) · [OpenAlex](https://openalex.org/W7166839988)

**Citations:** 1 citations (OpenAlex, 2026-10-14) · [1](https://md.research.elamur.ai/free/en/papers/mu-2026-llm-forcedaligner-non-autoregressive-accurate/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Forced alignment (FA) assigns start and end times to each word or character of a known transcript. Existing aligners such as MFA, NFA, CIF and WhisperX depend on language-specific resources and tend to drift on long recordings. Speech LLMs that generate timestamps token by token can hallucinate and run slowly. The authors propose LLM-ForcedAligner, which recasts FA as slot filling. Special [time] tokens are placed in the transcript, and the model predicts a discrete time index for each slot, using speech embeddings from an AuT encoder together with the slotted text. Training details:
- Causal masking is applied with unshifted inputs and labels.
- Loss is computed only at slot positions.
- Labels are pseudo-timestamps produced by MFA.
- Slots are inserted randomly, so alignment can be requested at any chosen position.

Because inference is non-autoregressive, it is fast and avoids hallucinations. The approach covers 10 languages, code-switched speech and recordings up to 300 seconds. It reduces accumulated averaging shift (AAS) by 69–78% relative to prior aligners, with only a small increase in RTF. This makes it a single multilingual alternative to maintaining one aligner per language.

## Key points
- **Release and scope:** The checkpoint and inference code are released as Qwen/Qwen3-ForcedAligner-0.6B on Hugging Face. Supported languages are zh, en, fr, de, it, ja, ko, pt, ru and es, in any crosslingual mix.
- **Main result:** AAS drops by 69–78% relative to baselines (MFA, NFA, CIF, WhisperX) on multilingual, crosslingual and long-form audio up to 300 s.
- **Design choices:**
  - The best timestamp-token resolution is 80 ms, which matches the AuT encoder's frame rate. Finer resolution fits the MFA labels more closely but generalizes worse on human labels.
  - Applying dynamic slot insertion to about 50% of training samples gives the lowest AAS.
  - About 0.9B parameters is the best model size; larger models overfit the MFA label distribution.
- **Limitations:**
  - The human-annotated test set covers only Chinese.
  - The training data is unevenly distributed across languages, so AAS is higher for languages other than Chinese and English.

*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

- [Nanyang Technological University](https://md.research.elamur.ai/free/en/universities/nanyang-technological-university/) — University, Singapore, Singapore · 184 works · 27,484 citations
- [Northwestern Polytechnical University](https://md.research.elamur.ai/free/en/universities/northwestern-polytechnical-university/) — University, Xi'an, China · 15 works · 67 citations

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## Cite

Cite as: Bingshen Mu, Xian Shi, Xiong Wang, Hexin Liu, Jin Xu and Lei Xie. 2026. LLM-ForcedAligner: A Non-Autoregressive and Accurate LLM-Based Forced Aligner for Multilingual and Long-Form Speech. 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.

```bibtex
@inproceedings{mu2026llm,
  title = {{LLM-ForcedAligner: A Non-Autoregressive and Accurate LLM-Based Forced Aligner for Multilingual and Long-Form Speech}},
  author = {Bingshen Mu, Xian Shi, Xiong Wang, Hexin Liu, Jin Xu and Lei Xie},
  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.1174},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1174}
}
```

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## References

31 references, 8 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 1 November 2026*