  Speaker diarization is necessary for interpreting conversations transcribed
using automated speech recognition (ASR) tools. Despite significant
developments in diarization methods, diarization accuracy remains an issue.
Here, we investigate the use of large language models (LLMs) for diarization
correction as a post-processing step. LLMs were fine-tuned using the Fisher
corpus, a large dataset of transcribed conversations. The ability of the models
to improve diarization accuracy in a holdout dataset was measured. We report
that fine-tuned LLMs can markedly improve diarization accuracy. However, model
performance is constrained to transcripts produced using the same ASR tool as
the transcripts used for fine-tuning, limiting generalizability. To address
this constraint, an ensemble model was developed by combining weights from
three separate models, each fine-tuned using transcripts from a different ASR
tool. The ensemble model demonstrated better overall performance than each of
the ASR-specific models, suggesting that a generalizable and ASR-agnostic
approach may be achievable. We hope to make these models accessible through
public-facing APIs for use by third-party applications.
