Abstract
Speech Relation Extraction (SpeechRE) aims to extract relation triplets directly from speech. However, existing benchmark datasets rely heavily on synthetic data, lacking sufficient quantity and diversity of real human speech. Moreover, existing models also suffer from rigid single-order generation templates and weak semantic alignment, substantially limiting their performance. To address these challenges, we introduce CommonVoice-SpeechRE, a large-scale dataset comprising nearly 20,000 real-human speech samples from diverse speakers, establishing a new benchmark for SpeechRE research. Furthermore, we propose the Relation Prompt-Guided Multi-Order Generative Ensemble (RPG-MoGe), a novel framework that features: (1) a multi-order triplet generation ensemble strategy, leveraging data diversity through diverse element orders during both training and inference, and (2) CNN-based latent relation prediction heads that generate explicit relation prompts to guide cross-modal alignment and accurate triplet generation. Experiments show our approach outperforms state-of-the-art methods, providing both a benchmark dataset and an effective solution for real-world SpeechRE. The source code and dataset are publicly available at this https URL.
Abstract (translated)
Speech Relation Extraction（SpeechRE）的目标是从语音中直接提取关系三元组。然而，现有的基准数据集严重依赖于合成数据，缺少足够数量和多样性的真人语音样本。此外，现有模型还受到单一生成模板的限制，并且语义对齐能力较弱，这极大地限制了它们的表现。为了应对这些挑战，我们引入了CommonVoice-SpeechRE，这是一个大规模的数据集，包含近20,000个来自多样化发言人的真实人类语音样本，为SpeechRE研究建立了新的基准。 此外，我们还提出了Relation Prompt-Guided Multi-Order Generative Ensemble（RPG-MoGe），这是一种新型框架，具有以下特点：(1) 一个多阶三元组生成集成策略，在训练和推断过程中通过多样化的元素顺序利用数据多样性；(2) 基于CNN的潜在关系预测头，这些头部可以生成明确的关系提示以指导跨模态对齐，并准确生成三元组。实验表明我们的方法超越了当前最先进的方法，不仅提供了一个基准数据集，还为现实世界的SpeechRE问题提供了有效的解决方案。 源代码和数据集在以下网址公开：[此处应填写具体链接]。
URL
https://arxiv.org/abs/2509.08438
