Robust speaker verification system with anti-spoofing detection and DNN feature enhancement modules

Abstract

This thesis focuses on the robustness issues of speaker verification (SV) systems. Although current SV systems perform well under clean condition, their performance degrades dramatically under real-world uncontrolled environments. The reliability of current SV systems is also questionable under spoofing attacks. These pitfalls severely limit it's deployment in many applications. This thesis presents approaches to combat these two robustness issues, namely noise robustness and spoofing attacks. To address the noise robustness issue, the use of deep neural networks (DNN) as a feature compensation method in the front-end module of the SV system is proposed. The motivation to use DNN is due to its success in various related speech fields, and its ability to model nonlinear relationships between high dimensional input and output. In this work, DNN is used to convert noisy input features into clean features. The proposed method is evaluated using the benchmarking speaker recognition evaluation (SRE) 2010 dataset provided by the National Institute of Standards and Technology(NIST). To focus on the effect of feature pre-processing, the SV system is trained using noise free speech and evaluated on noise corrupted speech. Results show that the proposed DNN feature compensation improves the equal error rate (EER) by 2%-25% under different unseen noise types for various SNR levels. i It is a great pleasure for me to acknowledge the assistance and contributions of many individuals in making this dissertation a success. First and foremost, I would like to thank my supervisor, Dr. Chng Eng Siong (NTU), for his assistance, ideas, and feedbacks during the process of doing this dissertation. Without his guidance and support, this dissertation could not have been completed on time. Secondly, I would like to thank Dr. Li Haizhou (I 2 R) and Dr. Lee Kong Aik (I 2 R) for their invaluable guidance. Their constant encouragement helped me overcome the difficulties encountered in my research. Thirdly, I want to thank my colleagues at the speech group in NTU for their generous help. I want to thank Dr. Xiao Xiong, Mr. Chong Tze Yuang, especially Mr. Tian Xiaohai for many fruitful discussions. I also want to express my sincere thanks to Osho Gupta for his support and suggestions. Last but not the least, I wish to express my sincere gratitude to my family for their encouragement and moral support. I also place on record, my sense of gratitude to one and all, who directly or indirectly, have lend their hand in this venture.