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arXiv · 2608.16203

INSPIRE: A Benchmark for Instruction-Aware Speech Retrieval

Chen-An Li·Hung-yi Lee
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Abstract

Existing speech retrieval systems rely on fixed similarity matching and cannot adapt to diverse user intents. We introduce INSPIRE, the first benchmark for instruction-aware speech retrieval, in which natural-language instructions dynamically specify relevance criteria, including semantic content, speaker identity, speaking style, environmental sounds, and their combinations. We evaluate four retrieval paradigms: large audio-language models, cascaded pipelines, self-supervised speech models, and contrastive audio-language models. Our results reveal that no current method robustly handles all retrieval intents. Text-based approaches perform relatively better at semantic retrieval but struggle with paralinguistic attributes, while speech-based models are moderately better at capturing acoustic properties but falter at following instructions. These findings highlight the need for unified architectures capable of instruction-aware speech retrieval.

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BibTeXRIS

Chen-An Li, Hung-yi Lee. 2026-08-17. INSPIRE: A Benchmark for Instruction-Aware Speech Retrieval. https://arxiv.org/abs/2608.16203

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