INSPIRE: A Benchmark for Instruction-Aware Speech Retrieval
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 strug...
| Authors | Chen-An Li, Hung-yi Lee |
|---|---|
| Primary category | cs.SD |
| Published | 17 August 2026 |
| Last updated at source | 17 August 2026 |
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