Hagai Aronowitz

This paper presents models that add timestamps and speaker information to a given transcript using a non-autoregressive LLM-based architecture and achieves state-of-the-art timestamp accuracy and the best cpWER for speaker attribution.

Speaker-attributed ASR gets a serious boost from jointly training speaker cluster tags within a speech-aware LLM, outperforming traditional pipelines.