Currently, voice biometrics systems are attracting a growing interest driven by the need for new authentication modalities. The BioVoz project focuses on the reliability of these systems, threatened by various types of attacks, from a simple playback of prerecorded speech to more sophisticated variants such as impersonation based on voice conversion or synthesis. One problem in detecting spoofed speech is the lack of suitable models based on classical signal processing techniques. Therefore, the current trend is based on the use of deep neural networks, either for direct attack detection, or for obtaining deep feature vectors to represent the audio signals. However, these solutions raise many questions that are still unanswered and are the subject of the research proposed here. These include what spectral or temporal information should be used to feed the network, how to compensate for the effect of acoustic noise, what network architecture is appropriate, or what methodology should be used for training in order to provide the network with discriminative generalization capabilities. The present project focuses on the search for solutions to the aforementioned problems without forgetting a fundamental issue, little studied so far, such as the integration of fraud detection in the whole biometrics system.