DiarizationLM: Speaker Diarization Post-Processing with Large Language Models

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. Speaker diarization is the task of partitioning speech into homogeneous segments according to speaker identities, answering the question "who spoken when" [1, 2]. Typical speaker diarization systems can be roughly categorized into two groups: modularized systems and end-to-end systems. A modularized speaker diarization system usually consists of multiple separately trained components including voice activity detection (VAD) [3, 4, 5, 6], speaker turn detection [7, 8], speaker encoder [9, 10, 11], and a clustering algorithm, which can be either unsupervised [12, 13, 14, 15, 16, 17] or supervised [18, 19]. In many real world applications such as meeting summarization, call center analysis, mobile recorder apps [24], and video captioning, knowing "who spoke when" is not sufficient. Speaker labels are more interpretable and meaningful when they are associated with speech transcripts.