Integrating Audio, Visual, and Semantic Information for Enhanced Multimodal Speaker Diarization on Multi-party Conversation

ACL ARR 2025 February Submission3744 Authors

Abstract:

Speaker diarization aims to segment an audio stream into homogeneous partitions based on speaker identity, playing a crucial role in speech comprehension and analysis. Mainstream speaker diarization systems rely only on acoustic information, making the task particularly challenging in complex acoustic environments in real-world applications.
Recently, significant efforts have been devoted to audio-visual or audio-semantic multimodal modeling to enhance speaker diarization performance; however, these approaches still struggle to address the complexities of speaker diarization on spontaneous and unstructured multi-party conversations. To fully exploit meaningful dialogue patterns, we propose a novel multimodal approach that jointly utilizes audio, visual, and semantic cues to enhance speaker diarization. Our approach structures visual cues among active speakers and semantic cues in spoken content into a cohesive format known as pairwise constraints, and employs a semi-supervised clustering technique based on pairwise constrained propagation. Extensive experiments conducted on multiple multimodal datasets demonstrate that our approach effectively integrates audio-visual-semantic information into the clustering process for acoustic speaker embeddings and consistently outperforms state-of-the-art speaker diarization methods, while largely preserving the overall system framework.

Paper Type: Long

Research Area: Speech Recognition, Text-to-Speech and Spoken Language Understanding

Research Area Keywords: speaker diarization, speech and vision, spoken language understanding

Contribution Types: NLP engineering experiment

Languages Studied: English, Mandarin

Submission Number: 3744