Automatic Speaker Verification on Compressed Audio

Children's speaker verification in low and zero resource conditions

This article verifies the reliability of automatic speaker verification (ASV) systems on new synthesis methods based on deep neural networks. ASV systems are widely used and applied regarding secure and effective biometric authentication. On the other hand, the rapid deployment of ASV systems contributes to the increased attention of attackers with newer and more sophisticated spoofing methods. Until recently, speech synthesis of the reference speaker did not seriously compromise the latest ASV systems. This situation is changing with the deployment of deep neural networks into the synthesis process. Projects including WaveNet, Deep Voice, Voice Loop, and many others generate very natural and high-quality speech that may clone voice identity. We are slowly approaching an era where we will not be able to recognize a genuine voice from a synthesized one. Therefore, it is necessary to define the robustness of current ASV systems to new methods of voice cloning. In this article, well-known SVM and GMM as well as new CNN-based ASVs are applied and subjected to synthesized speech from Tacotron 2 with the WaveNet TTS system. The results of this work confirm our concerns regarding the reliability of ASV systems against synthesized speech.

Automatic Speaker Verification (ASV) systems are widely used for voice-based identity verification, but recent studies have shown that they are vulnerable to both over-the-line (OTL) and over-the-air (OTA) adversarial attacks. Although various detection methods have been proposed, their robustness against diverse OTA attack scenarios has not been systematically assessed. In this work, we present AdvSV 2.0, a large-scale, open-source dataset for adversarial attacks on ASV systems. AdvSV 2.0 contains 628k utterances (800 hours) spanning 8 ASV models, 4 attack methods, and 9 physical playback-recording device combinations, and includes both OTL and OTA adversarial examples. To better connect digital adversarial optimization with the physical OTA channel, we further propose an OTA attack generation method based on a Neural Replay Simulator (NRS), which simulates replay-channel distortions and improves OTA attack success rates by up to 33.4% after air transmission. To defend against these attacks, we propose CODA-OCC, a COntrastive Domain-Aligned One-Class Classifier. Built on a one-class classification paradigm, CODA-OCC is trained only on bona fide speech and does not rely on any white-box assumption about the ASV backend. Experimental results on AdvSV 2.0 show that CODA-OCC achieves an EER of 11.2% and an AUC of 0.95, outperforming the purification-based baseline by 8.6% absolute EER, and providing a robust foundation for studying OTA adversarial robustness in ASV.

With the development of spoofing technologies, automatic speaker verification (ASV) systems have encountered serious challenges on security. In order to address this problem, many anti-spoofing countermeasures have been explored. There are two intuitive recipes to protect an ASV system from spoofing. The first one is to use a cascaded structure where spoofing detection is performed firstly and ASV is subsequently conducted only on the attempts which have passed the spoofing detection. The other one is to perform spoofing detection and ASV jointly. The discriminate reliably of the joint system has been proven to be more advantageous than cascaded systems with traditional methods, not only in accuracy, but also in convenience and computational efficiency. In this paper, we proposed a multi-task learning approach based on deep neural network to make a joint system of ASV and anti-spoofing. The performance of different acoustic features and structures of deep neural networks has been investigated on the ASVspoof 2017 version 2.0 dataset. The experimental results showed that the joint equal error rate (EER) of our approach was reduced by 0.55% compared to a joint system with Gaussian back-end fusion baseline.

With the development of speech synthesis technology, automatic speaker verification (ASV) systems have encountered the serious challenge of spoofing attacks. In order to improve the security of ASV systems, many antispoofing countermeasures have been developed. In the front-end domain, much research has been conducted on finding effective features which can distinguish spoofed speech from genuine speech and the published results show that dynamic acoustic features work more effectively than static ones. In the back-end domain, Gaussian mixture model (GMM) and deep neural networks (DNNs) are the two most popular types of classifiers used for spoofing detection. The log-likelihood ratios (LLRs) generated by the difference of human and spoofing log-likelihoods are used as spoofing detection scores. In this paper, we train a five-layer DNN spoofing detection classifier using dynamic acoustic features and propose a novel, simple scoring method only using human log-likelihoods (HLLs) for spoofing detection. We mathematically prove that the new HLL scoring method is more suitable for the spoofing detection task than the classical LLR scoring method, especially when the spoofing speech is very similar to the human speech. We extensively investigate the performance of five different dynamic filter bank-based cepstral features and constant Q cepstral coefficients (CQCC) in conjunction with the DNN-HLL method. The experimental results show that, compared to the GMM-LLR method, the DNN-HLL method is able to significantly improve the spoofing detection accuracy. Compared with the CQCC-based GMM-LLR baseline, the proposed DNN-HLL model reduces the average equal error rate of all attack types to 0.045%, thus exceeding the performance of previously published approaches for the ASVspoof 2015 Challenge task. Fusing the CQCC-based DNN-HLL spoofing detection system with ASV systems, the false acceptance rate on spoofing attacks can be reduced significantly.

The usage of Automatic Speaker Verification (ASV) in educational contexts has grown as a result of measurements of the speech signal in the claimed identity's native tongue. Even though resource-rich languages have received a lot of attention, low-resource languages, like Punjabi, still require work to be done on the development of ASV systems. In this study, an effort has been put forth to create an ASV system based on an i-vector for children while authenticating the student's dialects in Punjab region. Additionally, it has been determined how changing the Universal Background Model (UBM) size and i-vector dimensionality for the proposed system would affect the performance measure i.e. Equal Error Rate (EER). Finally, the EER values for the Malwa and Majha regions were lower than expected, at 8.47% and 9.26%, respectively. However, the output shows a large EER of 14.25% in Doaba region in comparison to that of Malwa and Majha regions.

This paper explores the application of active learning to enhance machine learning classifiers for spoofing detection in automatic speaker verification (ASV) systems. Leveraging the ASVspoof 2019 database, we integrate an active learning framework with traditional machine learning workflows, specifically focusing on Random Forest (RF) and Multilayer Perceptron (MLP) classifiers. The active learning approach was implemented by initially training models on a small subset of data and iteratively selecting the most uncertain samples for further training, which allowed the classifiers to refine their predictions effectively. Experimental results demonstrate that while the MLP initially outperformed RF with an accuracy of 95.83% compared to 91%, the incorporation of active learning significantly improved RF's performance to 94%, narrowing the performance gap between the two models. After applying active learning, both classifiers showed enhanced precision, recall, and F1-scores, with improvements ranging from 3% to 5%. This study provides valuable insights into the role of active learning in boosting the efficiency of machine learning models for dynamic spoofing scenarios in ASV systems. Future research should focus on designing advanced active learning techniques and exploring their integration with other machine learning paradigms to further enhance ASV security.

Deep generative variational autoencoding for replay spoof detection in automatic speaker verification

With the growing number of devices that use voice biometric system for verification purposes, it becomes mandatory to accurately verify the target speaker. Moreover, intruders use different spoofing attacks to fool an automatic speaker verification (ASV) system. Voice replay attack being the easiest to generate among other spoofing attacks is frequently employed in front of the ASV systems of these devices to get access to the system for malicious purposes. Thus, we need to develop robust systems that are capable of classifying the target speaker and differentiate between the bonafide and replay voice. To combat the vulnerabilities of ASV systems, we proposed an automatic speaker verification and replay attack detection (ASVRAD) system. For this purpose, we proposed a novel audio feature i.e., Glottal Flow Gammatone Cepstrum Coefficients (GLGTCC), an enhanced version of Glottal Flow Cepstrum Coefficients (GLFCC) for accurate classification of the bonafide speakers and voice replay attacks detection. Our GLGTCC features can reliably capture the speaker-specific attributes from their vocal excitation patterns in the audio. Later, we employed a multiclass support vector machine (SVM) to classify the bonafide speakers, and a binary SVM for classification of bonafide and replay audios. Experimental results of our system on the standard ASVspoof 2019 PA dataset achieved the lowest EER of 3.00% for speaker verification and 18.38% on voice replay attacks over the comparative methods.

Any biometric recognizer is vulnerable to direct spoofing attacks and automatic speaker verification (ASV) is no exception; replay, synthesis and conversion attacks all provoke false acceptances unless countermeasures are used. We focus on voice conversion (VC) attacks. Most existing countermeasures use full knowledge of a particular VC system to detect spoofing. We study a potentially more universal approach involving generative modeling perspective. Specifically, we adopt standard ivector representation and probabilistic linear discriminant analysis (PLDA) back-end for joint operation of spoofing attack detector and ASV system. As a proof of concept, we study a vocoder-mismatched ASV and VC attack detection approach on the NIST 2006 speaker recognition evaluation corpus. We report stand-alone accuracy of both the ASV and countermeasure systems as well as their combination using score fusion and joint approach. The method holds promise. Index Terms: speaker recognition, spoofing, voice conversion attack, i-vector, joint verification and anti-spoofing

Detection of replay spoof speech using teager energy feature cues

In recent years, speaker verification technologies have received an extensive amount of attention. Designing and developing machines that could communicate with humans are believed to be one of the primary motivations behind such developments. Speaker verification technologies are applied to numerous fields such as security, Biometrics, and forensics. In this paper, the authors study the effects of different languages on the performance of the automatic speaker verification (ASV) system. The MirasVoice speech corpus (MVSC), a bilingual English and Farsi speech corpus, is used in this study. This study collects results from both an I-vector based ASV system and a GMM-UBM based ASV system. The experimental results show that a mismatch between the enrolled data used for training and verification data can lead to a significant decrease in the overall system efficiency. This study shows that it is best to use an i-vector based framework with data from the English language used in the enrollment phase to improve the robustness of the ASV systems. The achieved results in this study indicate that this can narrow the degradation gap caused by the language mismatch.

The replay attack is refereed as an unauthorized attempt to access the automatic speaker verification (ASV) system by using the pre-recorded speech samples of any target. The replay attack is performed by placing the pre-recorded speech sample of the target before the machine. Of late the replay attack is identified as the greatest threat to ASV system, mainly due to the availability of high quality recording and playback devices. In this work, excitation source feature referred as glottal mel frequency cepstral coefficient (GMFCC) and shifted constant Q cepstral coefficient (SCQCC) are proposed for detection of replay signals. The GMFCC is derived by applying conventional mel-cepstral technique to glottal flow derivative signal. The SCQCC is computed by using constant Q cepstral processing. The effectiveness of the proposed features are demonstrated by conducting experiments with ASVspoof 2017 version 2.0 database. The proposed GMFCC feature provides an equal error rate (EER) of 16.78%, that is 19.63% higher than the recently proposed residual mel frequency cepstral coefficient(RMFCC) feature. The conventional CQCC feature provides an EER of 12.32%. The proposed SCQCC feature provides an EER of 11.34%, shows a relative improvement of 7.94% over CQCC. Further, the CQCC in together with proposed GMFCC provides an EER of 8.82%. On the other hand, the proposed SCQCC+GMFCC system provides an EER of 8.60%. These results signify the usefulness of the proposed system to counter replay attacks.

The state-of-art models for speech synthesis and voice conversion are capable of generating synthetic speech that is perceptually indistinguishable from bonafide human speech. These methods represent a threat to the automatic speaker verification (ASV) systems. Additionally, replay attacks where the attacker uses a speaker to replay a previously recorded genuine human speech are also possible. We present our solution for the ASVSpoof2019 competition, which aims to develop countermeasure systems that distinguish between spoofing attacks and genuine speeches. Our model is inspired by the success of residual convolutional networks in many classification tasks. We build three variants of a residual convolutional neural network that accept different feature representations (MFCC, Log-magnitude STFT, and CQCC) of input. We compare the performance achieved by our model variants and the competition baseline models. In the logical access scenario, the fusion of our models has zero t-DCF cost and zero equal error rate (EER), as evaluated on the development set. On the evaluation set, our model fusion improves the t-DCF and EER by 25% compared to the baseline algorithms. Against physical access replay attacks, our model fusion improves the baseline algorithms t-DCF and EER scores by 71% and 75% on the evaluation set, respectively.

In the last few years, we have witnessed an exponential growth in voice spoofing attacks. The intruders employ different types of attacks such as speech synthesis where they use the machine generated speech against any target person to fool the automatic speaker verification (ASV) systems for various tasks i.e. home control, bank account access, etc. The availability of modern-day advanced tools has made it convenient to launch such types of voice spoofing attacks. To overcome the challenges associated with bypassing the security of ASV systems using the synthetic speech, we propose an effective synthetic speech detector using a fusion of spectral features. More specifically, we propose a fused feature vector consisting of MFCC, GTCC, Spectral Flux, and Spectral Centroid for audio signal representation. This fused feature set is capable of capturing the traits of speech variation attributes of genuine signal and algorithmic artifacts of synthetic signals. These features are further used to train the bilstm to classify the signal as genuine or spoof. The proposed framework is capable of detecting both the voice conversion and synthetic speech attacks on ASV systems. Performance of our framework is evaluated on ASVspoof 2019 LA dataset. Our experimental results illustrate the effectiveness of the proposed framework for logical access attacks (voice conversion and cloned/synthetic voice) detection.