用分层索引加速语音关键词检索，速度更快且不丢精度。
H-QuEST: Accelerating Query-by-Example Spoken Term Detection with Hierarchical Indexing
- 用TF-IDF稀疏表示+分层小世界索引加速匹配
- 在多个数据集上提速10倍以上，准确率不变
- 适合大规模语音库的快速关键词搜索
查询式语音关键词检测（QbE-STD）通过语音样本在音频数据集中查找匹配词或短语。当缺乏标注数据时，通常采用动态时间规整（DTW）等模板匹配方法，但计算开销大、难以扩展。本文提出H-QuEST框架，利用基于词频-逆文档频率（TF-IDF）的稀疏表示，结合先进的音频表征学习与分层可导航小世界（HNSW）索引进行加速检索，并辅以精炼优化。实验表明，相比现有方法，H-QuEST在保持准确率的前提下显著提升检索速度，在多个数据集上提速超10倍。
原文摘要 · Abstract (English)
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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