Large language models (LLMs) have shown great promise for capturing contextual information in natural language processing tasks. We propose a novel approach to speaker diarization that incorporates the prowess of LLMs to exploit contextual cues in human dialogues. Our method builds upon an acoustic-based speaker diarization system by adding lexical information from an LLM in the inference stage. We model the multi-modal decoding process probabilistically and perform joint acoustic and lexical beam search to incorporate cues from both modalities: audio and text. Our experiments demonstrate that infusing lexical knowledge from the LLM into an acoustics-only diarization system improves overall speaker-attributed word error rate (SA-WER). The experimental results show that LLMs can provide complementary information to acoustic models for the speaker diarization task via proposed beam search decoding approach showing up to 39.8% relative delta-SA-WER improvement from the baseline system. Thus, we substantiate that the proposed technique is able to exploit contextual information that is inaccessible to acoustics-only systems which is represented by speaker embeddings. In addition, these findings point to the potential of using LLMs to improve speaker diarization and other speech processing tasks by capturing semantic and contextual cues.

翻译：大语言模型（LLM）在自然语言处理任务中展现出捕捉上下文信息的强大潜力。我们提出了一种新颖的说话人日志方法，该方法融合了LLM的能力以利用人类对话中的上下文线索。我们的方法在基于声学的说话人日志系统基础上，于推理阶段引入来自LLM的词汇信息。我们对多模态解码过程进行概率建模，并执行联合声学-词汇束搜索，以融合来自音频和文本两种模态的线索。实验表明，将LLM的词汇知识注入纯声学说话人日志系统，能够改善总体说话人属性词错误率（SA-WER）。实验结果证明，通过所提出的束搜索解码方法，LLM能为声学模型提供互补信息，在说话人日志任务中实现相对于基线系统高达39.8%的相对Δ-SA-WER改进。因此，我们证实该技术能够利用声学系统无法获取的上下文信息（即说话人嵌入所表征的信息）。此外，这些发现揭示了利用LLM捕捉语义与上下文线索以改进说话人日志及其他语音处理任务的潜力。