提升语音检索的准确与效率，增强抗噪能力。
BEST-STD2.0: Balanced and Efficient Speech Tokenizer for Spoken Term Detection
- 通过噪声和混响增强训练提升分词器鲁棒性。
- 引入最优传输正则化实现分词使用均衡，提高效率。
- 结合TF-IDF加速检索，适合实时语音搜索场景。
快速准确的语音内容检索对语音搜索等应用至关重要。查询式语音片段检测（Query-by-Example Spoken Term Detection, STD）旨在给定一段语音查询时，从音频数据库中检索出匹配片段。基于分词的STD系统利用离散语音表示实现高效检索，但在噪声和混响环境下表现不佳，且分词使用效率低。本文提出一种噪声与混响增强训练策略，提升分词器鲁棒性；引入基于最优传输的正则化方法，确保分词使用均衡，提升分词效率；并采用TF-IDF-based检索机制进一步加速搜索。实验证明，所提方法在多种失真条件下均优于现有基线，同时保持高检索效率。
原文摘要 · Abstract (English)
Fast and accurate spoken content retrieval is vital for applications such as voice search. Query-by-Example Spoken Term Detection (STD) involves retrieving matching segments from an audio database given a spoken query. Token-based STD systems, which use discrete speech representations, enable efficient search but struggle with robustness to noise and reverberation, and with inefficient token utilization. We address these challenges by proposing a noise and reverberation-augmented training strategy to improve tokenizer robustness. In addition, we introduce optimal transport-based regularization to ensure balanced token usage and enhance token efficiency. To further speed up retrieval, we adopt a TF-IDF-based search mechanism. Empirical evaluations demonstrate that the proposed method outperforms STD baselines across various distortion levels while maintaining high search efficiency.
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