Trustworthy speaker recognition with minimal prior knowledge using neural networks

The performance of speaker recognition systems has considerably improved in the last decade. This is mainly due to the development of Gaussian mixture model-based systems and in particular to the use of i-vectors. These systems handle relatively well noise and channel mismatches and yield a low error rate when confronted with zero-effort impostors, i.e. impostors using their own voice but claiming to be someone else. However, speaker verification systems are vulnerable to more sophisticated attacks, called presentation or spoofing attacks. In that case, the impostors present a fake sample to the system, which can either be generated with a speech synthesis or voice conversion algorithm or can be a previous recording of the target speaker. One way to make speaker recognition systems robust to this type of attack is to integrate a presentation attack detection system. Current methods for speaker recognition and presentation attack detection are largely based on short-term spectral processing. This has certain limitations. For instance, state-of-the-art speaker verification systems use cepstral features, which mainly capture vocal tract system characteristics, although voice source characteristics are also speaker discriminative. In the case of presentation attack detection, there is little prior knowledge that can guide us to differentiate bona fide samples from presentation attacks, as they are both speech signals that carry the same high level information, such as message, speaker identity and information about environment. This thesis focuses on developing speaker verification and presentation attack detection systems that rely on minimal assumptions. Towards that, inspired by recent advances in deep learning, we first develop speaker verification approaches where speaker discriminative information is learned from raw waveforms using convolutional neural networks (CNNs). We show that such approaches are capable of learning both voice source related and vocal tract system related speaker discriminative information and yield performance competitive to state of the art systems, namely i-vectors and x-vectors-based systems. We then develop two high performing approaches for presentation attack detection: one based on long-term spectral statistics and the other based on raw speech modeling using CNNs. We show that these two approaches are complementary and make the speaker verification systems robust to presentation attacks. Finally, we develop a visualization method inspired from the computer vision community to gain insight about the task-specific information captured by the CNNs from the raw speech signals.