Native Speech Processing with LLMs

Recent advances in Large Language Models (LLMs) have achieved state-of-the-art performance in Automatic Speech Recognition (ASR), surpassing ASR-only systems such as Whisper. However, their application to other speech processing tasks, particularly speaker diarisation (SD), remains underexplored. This work proposes extending existing speech-aware LLM architectures with diarisation-specific training and context-based prompting to enable joint transcription and segmentation of multi-speaker audio. By exploiting the semantic reasoning and multilingual capabilities of pretrained LLMs, the proposed approach aims to improve diarisation accuracy, enhancing accessibility for assistive technologies and real-time captioning applications that rely on accurate speaker-aware transcriptions.

Authors

• Aaron Soh Nanyang Technological University College of Computing and Data Science Singapore

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