Multisensor fusion techniques for tactical speaker recognition
Laurie H. Fenstermacher · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1995
Multi-sensor fusion techniques have been widely used for target and object recognition, but are relatively unheard of in the speech processing community. Multi-sensor fusion deals with the combination of complementary, and sometimes contradictory, sensor data into a reliable estimate of the environment to achieve a sum which is better than the parts. Rome Laboratory developed a tactical speaker recognition algorithm which incorporates both feature and classifier fusion. The strategy is to exploit the fact that different classifies err in different ways and multiple features, like multiple sensors, can improve recognition performance over the performance of any one feature set (or even a composite feature set). The feature sets used are LPC cepstra, Hamming liftered cepstra, RASTA liftered cepstra, and delta cepstra. Each feature set is used to train separate classifiers: K Nearest Neighbor, Hypersphere, Multilayer Perceptron and Vector Quantization classifiers. This paper details experiments whereby the results of each feature and classifier pair are fused using two different methods: a simple voting scheme (or majority strategy) and a weighted voting scheme. Results are quoted on a simulated data set and the Rome Laboratory Greenflag tactical database.