# Speech-to-See: End-to-End Speech-Driven Open-Set Object Detection Source: [Speech-to-See: End-to-End Speech-Driven Open-Set Object Detection]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:57:21.821Z Authors: Wenhuan Lu, Xinyue Song, Wenjun Ke, Zhizhi Yu, Wenhao Yang, Jianguo Wei Published: 2025-09-20T12:43:46.000Z ## Abstract Audio grounding, or speech-driven open-set object detection, aims to localize and identify objects directly from speech, enabling generalization beyond predefined categories. This task is crucial for applications like human-robot interaction where textual input is impractical. However, progress in this domain faces a fundamental bottleneck from the scarcity of large-scale, paired audio-image data, and is further constrained by previous methods that rely on indirect, text-mediated pipelines. In this paper, we introduce Speech-to-See (Speech2See), an end-to-end approach built on a pre-training and fine-tuning paradigm. Specifically, in the pre-training stage, we design a Query-Guided Semantic Aggregation module that employs learnable queries to condense redundant speech embeddings into compact semantic representations. During fine-tuning, we incorporate a parameter-efficient Mixture-of-LoRA-Experts (MoLE) architecture to achieve deeper and more nuanced cross-modal adaptation. Extensive experiments show that Speech2See achieves robust and adaptable performance across multiple benchmarks, demonstrating its strong generalization ability and broad applicability. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()