Automatic speaker verification (ASV) systems are subject to various kinds of malicious attacks. Replay, voice conversion and speech synthesis attacks drastically degrade the performance of a standard ASV system by increasing its false acceptance rates. This issue raised a high level of interest in the speech research community where the possible voice spoofing attacks and their related countermeasures have been investigated. However, much less effort has been devoted in creating realistic and diverse spoofing attack databases that foster researchers to correctly evaluate their countermeasures against attacks. The existing studies are not complete in terms of types of attacks, and often difficult to reproduce because of unavailability of public databases. In this paper we introduce the voice spoofing data-set of AVspoof, a public audio-visual spoofing database. AVspoof includes ten realistic spoofing threats generated using replay, speech synthesis and voice conversion. In addition, we provide a set of experimental results that show the effect of such attacks on current state-of-the-art ASV systems.

Speech Emotion Recognition (SER) has been shown to benefit from many of the recent advances in deep learning, including recurrent based and attention based neural network architectures as well. Nevertheless, performance still falls short of that of humans. In this work, we investigate whether SER could benefit from the self-attention and global windowing of the transformer model. We show on the IEMOCAP database that this is indeed the case. Finally, we investigate whether using the distribution of, possibly conflicting, annotations in the training data, as soft targets could outperform a majority voting. We prove that this performance increases with the agreement level of the annotators.

In this work, we study the use of attention mechanisms to enhance the performance of the state-of-the-art deep learning model in Speech Emotion Recognition (SER). We introduce a new Long Short-Term Memory (LSTM)-based neural network attention model which is able to take into account the temporal information in speech during the computation of the attention vector. The proposed LSTM-based model is evaluated on the IEMOCAP dataset using a 5-fold cross-validation scheme and achieved 68.8% weighted accuracy on 4 classes, which outperforms the state-of-the-art models.

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Using phonological speech vocoding, we propose a platform for exploring relations between phonology and speech processing, and in broader terms, for exploring relations between the abstract and physical structures of a speech signal. Our goal is to make a step towards bridging phonology and speech processing and to contribute to the program of Laboratory Phonology. We show three application examples for laboratory phonology: compositional phonological speech modelling, a comparison of phonological systems and an experimental phonological parametric text-to-speech (TTS) system. The featural representations of the following three phonological systems are considered in this work: (i) Government Phonology (GP), (ii) the Sound Pattern of English (SPE), and (iii) the extended SPE (eSPE). Comparing GP- and eSPE-based vocoded speech, we conclude that the latter achieves slightly better results than the former. However, GP - the most compact phonological speech representation - performs comparably to the systems with a higher number of phonological features. The parametric TTS based on phonological speech representation, and trained from an unlabelled audiobook in an unsupervised manner, achieves intelligibility of 85% of the state-of-the-art parametric speech synthesis. We envision that the presented approach paves the way for researchers in both fields to form meaningful hypotheses that are explicitly testable using the concepts developed and exemplified in this paper. On the one hand, laboratory phonologists might test the applied concepts of their theoretical models, and on the other hand, the speech processing community may utilize the concepts developed for the theoretical phonological models for improvements of the current state-of-the-art applications