通过量化嵌入改善噪声下少样本关键词检测的阈值选择
Quantization-Based Score Calibration for Few-Shot Keyword Spotting with Dynamic Time Warping in Noisy Environments
- 在嵌入层对特征进行量化，利用量化误差归一化
- 在模拟高频无线电信道上提升检测准确率
- 适合部署在噪声环境下的少样本关键词识别系统
关键词检测（KWS）系统需对连续检测分数进行阈值判断。传统方法依赖验证集优化阈值，但在未知或噪声环境下、特别是在少样本设置中常表现不佳。本文研究基于动态时间规整（DTW）的模板式开集少样本KWS在噪声语音中的阈值估计问题。为缓解次优阈值导致的性能下降，提出一种在嵌入层操作的分数校准方法：通过量化学习到的表示，并在DTW评分与阈值判断前应用基于量化误差的归一化。在包含模拟高频无线电通道的KWS-DailyTalk数据集上的实验表明，该校准方法简化了鲁棒阈值的选择，显著提升了检测性能。
原文摘要 · Abstract (English)
Detecting occurrences of keywords with keyword spotting (KWS) systems requires thresholding continuous detection scores. Selecting appropriate thresholds is a non-trivial task, typically relying on optimizing performance on a validation dataset. However, such greedy threshold selection often leads to suboptimal performance on unseen data, particularly in varying or noisy acoustic environments or few-shot settings. In this work, we investigate detection threshold estimation for template-based open-set few-shot KWS using dynamic time warping on noisy speech data. To mitigate the performance degradation caused by suboptimal thresholds, we propose a score calibration approach that operates at the embedding level by quantizing learned representations and applying quantization error-based normalization prior to DTW-based scoring and thresholding. Experiments on KWS-DailyTalk with simulated high frequency radio channels show that the proposed calibration approach simplifies the selection of robust detection thresholds and significantly improves the resulting performance.
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