DiarizationLM: Speaker Diarization Post-Processing with Large Language
  Models
Authors: name: Quan Wang, name: Yiling Huang, name: Guanlong Zhao, name: Evan Clark, name: Wei Xia, name: Hank Liao

In this paper, we introduce DiarizationLM, a framework to leverage large
language models (LLM) to post-process the outputs from a speaker diarization
system. Various goals can be achieved with the proposed framework, such as
improving the readability of the diarized transcript, or reducing the word
diarization error rate (WDER). In this framework, the outputs of the automatic
speech recognition (ASR) and speaker diarization systems are represented as a
compact textual format, which is included in the prompt to an optionally
finetuned LLM. The outputs of the LLM can be used as the refined diarization
results with the desired enhancement. As a post-processing step, this framework
can be easily applied to any off-the-shelf ASR and speaker diarization systems
without retraining existing components. Our experiments show that a finetuned
PaLM 2-S model can reduce the WDER by rel. 25.9% on the Fisher telephone
conversation dataset, and rel. 31% on the Callhome English dataset.