The voice sensor is the core part of voice monitoring devices, and it is commonly drifted in long-term running. For this reason, the voice calibration of monitoring devices is essential. Several calibration methods had been introduced by leveraging expensive referred instruments or manual calibration methods. However, these methods are not only dependent on high-cost instruments, but also is impractical on isolated occasions. To overcome these issues, the feature fusion-based neighbor (FbN) model is proposed to calibrate voice sensors, via real-time low-cost ambient sensors. The FbN consists of a real-time awareness stage, feature selection stage, feature fusion stage, and prediction stage. First, voice data and exogenous low-cost sensor (LCS) data are simultaneously collected. Second, those low-cost sensor data are treated as individual features. The irrelevant features are empirically filtered out. The adopted exogenous features are temperature, humidity and air pressure. Third, the selected features are fused to obtain more representative features. Finally, distances between sensor data and represented features are calculated and sorted. The top-$k$ average distances are regarded as the predictive results. Experimental comparisons with several novelty methods show the effectiveness of the proposed FbN.

语音传感器是语音监控设备的核心部件，在长期运行中容易出现漂移现象。为此，监控设备的语音校准必不可少。通过利用昂贵的参考仪器或手动校准方法引入了几种校准方法。然而，这些方法不仅依赖于高成本的仪器，而且在孤立的场合也不切实际。为了克服这些问题，提出了基于特征融合的邻居 (FbN) 模型来通过实时低成本环境传感器校准语音传感器。FbN 由实时感知阶段、特征选择阶段、特征融合阶段和预测阶段组成。首先，同时收集语音数据和外源低成本传感器（LCS）数据。其次，那些低成本的传感器数据被视为独立的特征。根据经验过滤掉不相关的特征。采用的外生特征是温度、湿度和气压。第三，对选择的特征进行融合，得到更具代表性的特征。最后，传感器数据和表示的特征之间的距离被计算和排序。顶端-$k$平均距离被视为预测结果。与几种新颖方法的实验比较表明了所提出的 FbN 的有效性。