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 the 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 consisting of two different steps: quantizing embeddings and normalizing detection scores using the quantization error prior to thresholding. Experiments on KWS-DailyTalk with simulated high frequency radio channels show that the proposed calibration approach simplifies the choice of detection thresholds and significantly improves the resulting performance.

翻译：关键词检测系统需要通过阈值化连续检测分数来识别关键词的出现。选择合适的阈值并非易事，通常依赖于在验证数据集上优化性能。然而，这种贪婪的阈值选择方法往往导致在未见数据上性能欠佳，尤其是在多变或嘈杂的声学环境或小样本设置中。本研究针对基于模板的开放集小样本关键词检测，在噪声语音数据上使用动态时间规整，探讨检测阈值估计问题。为缓解因次优阈值导致的性能下降，我们提出一种分数校准方法，该方法包含两个不同步骤：在阈值化之前，对嵌入进行量化，并利用量化误差对检测分数进行归一化。在KWS-DailyTalk数据集上模拟高频无线电信道的实验表明，所提出的校准方法简化了检测阈值的选择，并显著提升了最终性能。