Replay attacks presents a great risk for Automatic Speaker Verification (ASV) system. In this paper, we propose a novel replay detector based on Variable length Teager Energy Operator-Energy Separation Algorithm-Instantaneous Frequency Cosine Coefficients (VESA-IFCC) for the ASV spoof 2017 challenge.The key idea here is to exploit the contribution of IF in each subband energy via ESA to capture possible changes in spectral envelope (due to transmission and channel characteristics of replay device) of replayed speech. The IF is computed from narrowband components of speech signal, and DCT is applied in IF to get proposed feature set. We compare the performance of the proposed VESA-IFCC feature set with the features developed for detecting synthetic and voice converted speech. This includes the CQCC, CFCCIF and prosody-based features. On the development set, the proposed VESA-IFCC features when fused at score-level with a variant of CFCCIF and prosodybased features gave the least EER of 0.12 %. On the evaluation set, this combination gave an EER of 18.33 %. However, post-evaluation results of challenge indicate that VESA-IFCC features alone gave the relatively least EER of 14.06 % (i.e., relatively 16.11 % less compared to baseline CQCC) and hence, is a very useful countermeasure to detect replay attacks.Variable length Teager Energy Operator (VTEO) is the modified version of the traditional TEO method [27]. TEO involves nonlinear operations on the signal, i.e, square of current sample and multiplication of previous and next sample, i.e., x(n − 1)

In recent years, automatic speaker verification (ASV) is used extensively for voice biometrics. This leads to an increased interest to secure these voice biometric systems for real-world applications. The ASV systems are vulnerable to various kinds of spoofing attacks, namely, synthetic speech (SS), voice conversion (VC), replay, twins, and impersonation. This paper provides the literature review of ASV spoof detection, novel acoustic feature representations, deep learning, end-to-end systems, etc. Furthermore, the paper also summaries previous studies of spoofing attacks with emphasis on SS, VC, and replay along with recent efforts to develop countermeasures for spoof speech detection (SSD) task. The limitations and challenges of SSD task are also presented. While several countermeasures were reported in the literature, they are mostly validated on a particular database, furthermore, their performance is far from perfect. The security of voice biometrics systems against spoofing attacks remains a challenging topic. This paper is based on a tutorial presented at APSIPA Annual Summit and Conference 2017 to serve as a quick start for those interested in the topic.

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In this paper, we present a standalone replay spoof speech detection (SSD) system to classify the natural vs. replay speech. The replay speech spectrum is known to be affected in the higher frequency range. In this context, we propose to exploit an auditory filterbank learning using Convolutional Restricted Boltzmann Machine (ConvRBM) with the pre-emphasized speech signals. Temporal modulations in amplitude (AM) and frequency (FM) are extracted from the ConvRBM subbands using the Energy Separation Algorithm (ESA). ConvRBM-based short-time AM and FM features are developed using cepstral processing, denoted as AM-ConvRBM-CC and FM-ConvRBM-CC. Proposed temporal modulation features performed better than the baseline Constant-Q Cepstral Coefficients (CQCC) features. On the evaluation set, an absolute reduction of 7.48 % and 5.28 % in Equal Error Rate (EER) is obtained using AM-ConvRBM-CC and FM-ConvRBM-CC, respectively compared to our CQCC baseline. The best results are achieved by combining scores from AM and FM cues (0.82 % and 8.89 % EER for development and evaluation set, respectively). The statistics of AM-FM features are analyzed to understand the performance gap and complementary information in both the features.

Artefacts that serve to distinguish bona fide speech from spoofed or deepfake speech are known to reside in specific subbands and temporal segments. Various approaches can be used to capture and model such artefacts, however, none works well across a spectrum of diverse spoofing attacks. Reliable detection then often depends upon the fusion of multiple detection systems, each tuned to detect different forms of attack. In this paper we show that better performance can be achieved when the fusion is performed within the model itself and when the representation is learned automatically from raw waveform inputs. The principal contribution is a spectro-temporal graph attention network (GAT) which learns the relationship between cues spanning different sub-bands and temporal intervals. Using a model-level graph fusion of spectral (S) and temporal (T) sub-graphs and a graph pooling strategy to improve discrimination, the proposed RawGAT-ST model achieves an equal error rate of 1.06% for the ASVspoof 2019 logical access database. This is one of the best results reported to date and is reproducible using an open source implementation.