Automatic speaker verification (ASV) systems use a playback detector to filter out playback attacks and ensure verification reliability. Since current playback detection models are almost always trained using genuine and played-back speech, it may be possible to degrade their performance by transforming the acoustic characteristics of the played-back speech close to that of the genuine speech. One way to do this is to enhance speech "stolen" from the target speaker before playback. We tested the effectiveness of a playback attack using this method by using the speech enhancement generative adversarial network to transform acoustic characteristics. Experimental results showed that use of this "enhanced stolen speech" method significantly increases the equal error rates for the baseline used in the ASVspoof 2017 challenge and for a light convolutional neural network-based method. The results also showed that its use degrades the performance of a Gaussian mixture model-universal background model-based ASV system. This type of attack is thus an urgent problem needing to be solved.
There are a few attack methods similar to our enhanced stolen speech method. improved the naturalness of synthesized and converted speech before attacking a phase-based synthetic speech detector and an ASV system @cite_45 . The synthesized and converted speech signals were firstly analyzed frame by frame, and each frame was replaced with one containing the most similar natural speech selected from a dataset. A complex cepstrum vocoder was used to re-synthesize these frames so as to improve speech naturalness. Finally, the speech was directly fed into an ASV system. They reported that their method fooled four out of nine detectors. Our method can be thought of as an extension of their method as it further transforms synthesized speech close to natural speech.
@cite_45
2591404214 2963175699 2587891104 1544516254
State-of-the-art speaker verification systems are vulnerable to spoofing attacks. To address the issue, high-performance synthetic speech detectors (SSDs) for existing spoofing methods have been proposed. Phase-based SSDs that exploit the fact that most of the parametric speech coders use minimum-phase filters are particularly successful when synthetic speech is generated with a parametric vocoder. Here, we propose a new attack strategy to spoof phase-based SSDs with the objective of increasing the security of voice verification systems by enabling the development of more generalized SSDs. As opposed to other parametric vocoders, the complex cepstrum approach uses mixed-phase filters, which makes it an ideal candidate for spoofing the phase-based SSDs. We propose using a complex cepstrum vocoder as a postprocessor to existing techniques to spoof the speaker verification system as well as the phase-based SSDs. Once synthetic speech is generated with a speech synthesis or a voice conversion technique, for each synthetic speech frame, a natural frame is selected from a training database using a spectral distance measure. Then, complex cepstrum parameters of the natural frame are used for resynthesizing the synthetic frame. In the proposed method, complex cepstrum-based resynthesis is used as a postprocessor. Hence, it can be used in tandem with any synthetic speech generator. Experimental results showed that the approach is successful at spoofing four phase-based SSDs across nine parametric attack algorithms. Moreover, performance at spoofing the speaker verification system did not substantially degrade compared to the case when no postprocessor is employed. In hybrid hidden Markov model artificial neural networks (HMM ANN) automatic speech recognition (ASR) system, the phoneme class conditional probabilities are estimated by first extracting acoustic features from the speech signal based on prior knowledge such as, speech perception or and speech production knowledge, and, then modeling the acoustic features with an ANN. Recent advances in machine learning techniques, more specifically in the field of image processing and text processing, have shown that such divide and conquer strategy (i.e., separating feature extraction and modeling steps) may not be necessary. Motivated from these studies, in the framework of convolutional neural networks (CNNs), this paper investigates a novel approach, where the input to the ANN is raw speech signal and the output is phoneme class conditional probability estimates. On TIMIT phoneme recognition task, we study different ANN architectures to show the benefit of CNNs and compare the proposed approach against conventional approach where, spectral-based feature MFCC is extracted and modeled by a multilayer perceptron. Our studies show that the proposed approach can yield comparable or better phoneme recognition performance when compared to the conventional approach. It indicates that CNNs can learn features relevant for phoneme classification automatically from the raw speech signal. With the development of speech synthesis techniques, automatic speaker verification systems face the serious challenge of spoofing attack. In order to improve the reliability of speaker verification systems, we develop a new filter bank-based cepstral feature, deep neural network (DNN) filter bank cepstral coefficients, to distinguish between natural and spoofed speech. The DNN filter bank is automatically generated by training a filter bank neural network (FBNN) using natural and synthetic speech. By adding restrictions on the training rules, the learned weight matrix of FBNN is band limited and sorted by frequency, similar to the normal filter bank. Unlike the manually designed filter bank, the learned filter bank has different filter shapes in different channels, which can capture the differences between natural and synthetic speech more effectively. The experimental results on the ASVspoof 2015 database show that the Gaussian mixture model maximum-likelihood classifier trained by the new feature performs better than the state-of-the-art linear frequency triangle filter bank cepstral coefficients-based classifier, especially on detecting unknown attacks. Even the best statistical parametric speech synthesis systems do not achieve the naturalness of good unit selection. We investigated possible causes of this. By constructing speech signals that lie in between natural speech and the output from a complete HMM synthesis system, we investigated various effects of modelling. We manipulated the temporal smoothness and the variance of the spectral parameters to create stimuli, then presented these to listeners alongside natural and vocoded speech, as well as output from a full HMM-based text-to-speech system and from an idealised ‘pseudo-HMM’. All speech signals, except the natural waveform, were created using vocoders employing one of two popular spectral parameterisations: Mel-Cepstra or Mel-Line Spectral Pairs. Listeners made ‘same or different’ pairwise judgements, from which we generated a perceptual map using Multidimensional Scaling. We draw conclusions about which aspects of HMM synthesis are limiting the naturalness of the synthetic speech.