arXiv:2604.18360v2 Announce Type: replace-cross Abstract: Audio-text retrieval systems based on Contrastive Language-Audio Pretraining (CLAP) achieve strong performance on traditional benchmarks; however, these benchmarks rely on caption-style queries that differ substantially from real-world search behavior, limiting their assessment of practical retrieval robustness. We present Omni-Embed-Audio (OEA), a retrieval-oriented encoder leveraging multimodal LLMs with native audio understanding. To systematically evaluate robustness beyond caption-style queries, we introduce User-Intent Queries (UI
The rapid advancement and integration of multimodal AI necessitate robust retrieval systems that can handle real-world complexities beyond traditional benchmarks.
Improving audio-text retrieval with multimodal LLMs directly addresses shortcomings in current AI understanding of complex user intent, enhancing the practical utility and robustness of AI applications.
AI systems will be better equipped to interpret diverse audio inputs and user queries, leading to more accurate and user-friendly voice assistants, search engines, and autonomous systems.
- · AI developers
- · Voice assistant companies
- · Audio content platforms
- · Users of AI-powered services
- · Companies relying on basic keyword-based audio retrieval
- · Traditional audio processing methods
More sophisticated and nuanced AI interactions through improved audio understanding.
Accelerated development of AI agents capable of processing and responding to complex spoken commands and environmental audio cues.
New forms of human-computer interaction emerge, potentially reducing friction for diverse user groups and accelerating the adoption of AI-driven interfaces in daily life.
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Read at arXiv cs.CL