H-QuEST: Accelerating Query-by-Example Spoken Term Detection with Hierarchical Indexing

Akanksha Singh; Yi-Ping Phoebe Chen; Vipul Arora

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arXiv · 2506.16751

H-QuEST: Accelerating Query-by-Example Spoken Term Detection with Hierarchical Indexing

Akanksha Singh·Yi-Ping Phoebe Chen·Vipul Arora

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Abstract

Query-by-example spoken term detection (QbE-STD) searches for matching words or phrases in an audio dataset using a sample spoken query. When annotated data is limited or unavailable, QbE-STD is often done using template matching methods like dynamic time warping (DTW), which are computationally expensive and do not scale well. To address this, we propose H-QuEST (Hierarchical Query-by-Example Spoken Term Detection), a novel framework that accelerates spoken term retrieval by utilizing Term Frequency and Inverse Document Frequency (TF-IDF)-based sparse representations obtained through advanced audio representation learning techniques and Hierarchical Navigable Small World (HNSW) indexing with further refinement. Experimental results show that H-QuEST delivers substantial improvements in retrieval speed without sacrificing accuracy compared to existing methods.

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BibTeX RIS

Akanksha Singh, Yi-Ping Phoebe Chen, Vipul Arora. 2025-06-20. H-QuEST: Accelerating Query-by-Example Spoken Term Detection with Hierarchical Indexing. https://arxiv.org/abs/2506.16751

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