@inbook{471ac575f23f490f848ff8beae53a3c7,
author = "Aleksandr Diment and Tuomas Virtanen and Mikko Parviainen and Roman Zelov and Alex Glasman",
abstract = "Detection of whispered speech in the presence of high levels of background noise has applications in fraudulent behaviour recognition. For instance, it can serve as an indicator of possible insider trading. We propose a deep neural network (DNN)-based whispering detection system, which operates on both magnitude and phase features, including the group delay feature from all-pole models (APGD). We show that the APGD feature outperforms the conventional ones. Trained and evaluated on the collected diverse dataset of whispered and normal speech with emulated phone line distortions and significant amounts of added background noise, the proposed system performs with accuracies as high as 91.8%.",
booktitle = "24th European Signal Processing Conference (EUSIPCO)",
doi = "10.1109/EUSIPCO.2016.7760661",
keywords = "whispering; noise robustness; deep neural networks",
month = "8",
publisher = "IEEE",
title = "{N}oise-{R}obust {D}etection of {W}hispering in {T}elephone {C}alls {U}sing {D}eep {N}eural {N}etworks",
year = "2016",
}