# Word Timestamps and Speaker Attribution with a Non-Autoregressive LLM

arXiv: https://arxiv.org/abs/2609.15218 · HTML: https://mlsanity.com/paper/2609.15218/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2026.

## Built on by (most cited first, 1 of 1)

- [Where Words Begin and End](https://mlsanity.com/paper/2610.17782/) (2026)

## Abstract (arXiv)

Timestamps and speaker attribution are useful additions to speech recognition, creating a rich text transcript. This information can either be extracted during transcription or aligned to a given transcript. In this paper we present models that add timestamps and speaker information to a given transcript using a non-autoregressive LLM-based architecture. Compared to an autoregressive model built from similar components, the models are more accurate and annotate a given transcript one to two orders of magnitude faster. Compared to other models, our models achieve state-of-the-art timestamp accuracy and the best cpWER for speaker attribution.