Fundamentals of voice biometrics: classical and machine learning approaches learning approaches

Chapter Contents: 2.1 Introduction to speaker recognition systems 2.2 Metrics for system performance evaluation 2.2.1 ROC, DET and EER 2.2.2 Detection cost function 2.3 Text-independent speaker recognition 2.3.1 Classical acoustic approaches: GMM-UBM, i-vector and PLDA 2.3.2 DNN approaches 2.3.2.1 Basic concepts of neural networks 2.3.2.2 Some applications of DNNs to speech processing 2.3.3 DNNs for speaker recognition 2.4 Text-dependent speaker recognition 2.4.1 Classification of systems and techniques 2.4.2 Databases and benchmarks 2.5 Calibration of speaker recognition scores 2.5.1 Motivation: why to calibrate? 2.5.2 What is calibration? 2.5.3 Score-to-LR computation methods 2.5.3.1 Generative calibration models: fitting distributions to scores 2.5.3.2 Discriminative calibration models: transforming scores into LR values to optimize a cost function 2.5.4 Performance measurement of score-to-LR methods References