Constant Q Cepstral Coefficients Research Articles

Combination of Amplitude and Frequency Modulation Features for Presentation Attack Detection

In this paper, we propose the combination of Amplitude Modulation and Frequency Modulation (AM-FM) features for replay Spoof Speech Detection (SSD) task. The AM components are known to be affected by noise (in this case, due to replay mechanism). In particular, we exploit this damage in AM component to corresponding Instantaneous Frequency (IF) for SSD task. Thus, the novelty of proposed Amplitude Weighted Frequency Cepstral Coefficients (AWFCC) feature set lies in using frequency components along with squared weighted amplitude components that are degraded due to replay noise. The AWFCC feature set contains the information of both AM and FM components together and hence, gave discriminatory information in the spectral characteristics. The experiments were performed on publicly available ASVspoof 2017 challenge version 1.0 and 2.0 databases using AWFCC feature set. We have compared results of proposed feature set with the other state-of-the-art feature set, such as Constant Q Cepstral Coefficients (CQCC), Linear Frequency Cepstral Coefficients (LFCC), Mel Frequency Cepstral Coefficients (MFCC) and using a simple Gaussian Mixture Model (GMM) classifier. The individual performance of AWFCC feature set obtained lower % EER than the other feature sets on both version 1.0 and 2.0 databases. Furthermore, we used score-level fusion in order to obtain the possible complementary information of two feature sets to reduce the % EER further. To that effect, the score-level fusion of CQCC and AWFCC feature sets gave 5.75 % and 10.42 % EER on development and evaluation sets, respectively, of ASVspoof 2017 version 2.0 database. Moreover, for evaluation dataset, we have also studied the performance of proposed feature set on different Replay Configurations (RC), namely, acoustic environments, playback, and recording devices. For all the levels of threat conditions (i.e., low, medium, and high) to the ASV system, the proposed feature set performed better compared to the existing state-of-the-art feature sets.

Discriminative features based on modified log magnitude spectrum for playback speech detection

In order to improve the performance of hand-crafted features to detect playback speech, two discriminative features, constant-Q variance-based octave coefficients and constant-Q mean-based octave coefficients, are proposed for playback speech detection in this work. They rely on our findings that variance-based modified log magnitude spectrum and mean-based modified log magnitude spectrum can enhance the discriminative power between genuine speech and playback speech. Then constant-Q variance-based octave coefficients (constant-Q mean-based octave coefficients) can be obtained by combining variance-based modified log magnitude spectrum (mean-based modified log magnitude spectrum), octave segmentation, and discrete cosine transform. Finally, constant-Q variance-based octave coefficients and constant-Q mean-based octave coefficients are evaluated on ASVspoof 2017 corpus version 2.0 and ASVspoof 2019 physical access, respectively. Experimental results show that variance-based modified log magnitude spectrum and mean-based modified log magnitude spectrum can produce discriminative features toward playback speech. Further results on the two databases show that constant-Q variance-based octave coefficients and constant-Q mean-based octave coefficients can perform better than some common features, such as mel frequency cepstral coefficients and constant-Q cepstral coefficients.

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

Automatic speaker verification (ASV) systems are highly vulnerable to presentation attacks, also called spoofing attacks. Replay is among the simplest attacks to mount — yet difficult to detect reliably. The generalization failure of spoofing countermeasures (CMs) has driven the community to study various alternative deep learning CMs. The majority of them are supervised approaches that learn a human-spoof discriminator. In this paper, we advocate a different, deep generative approach that leverages from powerful unsupervised manifold learning in classification. The potential benefits include the possibility to sample new data, and to obtain insights to the latent features of genuine and spoofed speech. To this end, we propose to use variational autoencoders (VAEs) as an alternative backend for replay attack detection, via three alternative models that differ in their class-conditioning. The first one, similar to the use of Gaussian mixture models (GMMs) in spoof detection, is to train independently two VAEs — one for each class. The second one is to train a single conditional model (C-VAE) by injecting a one-hot class label vector to the encoder and decoder networks. Our final proposal integrates an auxiliary classifier to guide the learning of the latent space. Our experimental results using constant-Q cepstral coefficient (CQCC) features on the ASVspoof 2017 and 2019 physical access subtask datasets indicate that the C-VAE offers substantial improvement in comparison to training two separate VAEs for each class. On the 2019 dataset, the C-VAE outperforms the VAE and the baseline GMM by an absolute 9 - 10% in both equal error rate (EER) and tandem detection cost function (t-DCF) metrics. Finally, we propose VAE residuals — the absolute difference of the original input and the reconstruction as features for spoofing detection. The proposed frontend approach augmented with a convolutional neural network classifier demonstrated substantial improvement over the VAE backend use case.

Device Feature Extraction Based on Parallel Neural Network Training for Replay Spoofing Detection

A device feature, which contains information from both the recording channel and playback channel, is of paramount importance in replay spoofing detection. Presently, no technical reports, pertaining to the usage of device information for spoofing detection to achieve speaker verification, have been published. In this article, we propose to build a replay device feature (RDF) extractor on the basis of the genuine-replay-pair parallel neural network training database. The target of the neural network for such an RDF extractor is constrained in a spectrum domain such as discrete Fourier transform or constant-Q transform. Different types of neural networks are investigated for the parallel training framework. Finally the RDF extractor is formed by applying discrete cosine transform to the output vector of neural network. The bidirectional long short term memory gives the best performance among the investigated types of neural networks. The experimental results on ASVspoof 2017 corpus version 2.0 indicate that the equal error rate (EER) of a replay detection system with the proposed RDF has a value of 15.08%. Furthermore, by combining the RDF with constant-Q cepstral coefficients plus the log energy in score level, the EER of the detection system can be further reduced to 8.99%. In addition, the experimental results also reveal that the RDF exhibits great complementarity with many handcrafted features.

Extraction of Octave Spectra Information for Spoofing Attack Detection

This article focuses on extracting information from the octave power spectra of long-term constant-Q transform (CQT) for spoofing attack detection. A novel framework based on multi-level transform (MLT) is proposed that can capture the relevant information from octave power spectra using level by level in a multi-level manner. We then derive a novel feature referred to as constant-Q multi-level coefficient (CMC) based on proposed MLT. The proposed feature is evaluated on synthetic as well as replay speech detection studies on ASVspoof 2015 and ASVspoof 2017 version 2.0 database, respectively. We find the proposed CMC feature outperforms the conventional constant-Q cepstral coefficient based long-term feature obtained from linear power spectrum after uniform resampling. This depicts the usefulness of MLT to extract salient artifacts from octave power spectrum. Further, the proposed CMC feature performs better than the existing the well known other state-of-the-art systems for spoofing attack detection that showcases its importance.

Long-term high frequency features for synthetic speech detection

Over the recent years, detection of spoofing attack has become pivotal for automatic speaker verification systems. The long-term information has been found to be useful for the detection of synthetic speech attacks. In this work, we focus on investigating high frequency information from the long-term constant-Q transform (CQT) based features. For this purpose, first we derive two novel feature, namely, inverted constant-Q coefficients (ICQC) and inverted constant-Q cepstral coefficients (ICQCC) using discrete cosine transform (DCT) on inverted octave power spectrum and inverted linear power spectrum, respectively. Further, we extend the work by use of overlapped block transform with DCT which can avoid the features extracted from full frequency-band are easily affected by local noise in some particular frequency bands. The two other novel features thus derived from inverted octave blocks and inverted linear blocks are referred to as constant-Q block coefficients (ICBC) and inverted constant-Q linear block coefficients (ICLBC), respectively. We study the proposed features on ASVspoof 2015, noisy ASVspoof 2015 and ASVspoof 2019 logical access corpora. For the proposed four features, our experimental results showed that ICQBC, ICBC, ICQC provide the best performance on ASVspoof 2015, noisy ASVspoof 2015 and ASVspoof 2019 logical access databases, respectively.

Improving anti-spoofing with octave spectrum and short-term spectral statistics information

The long-term window based features have been found to be effective for spoofing attack detection. One such important countermeasure is constant-Q cepstral coefficients (CQCC) that is derived from constant-Q transform. During its extraction, the octave power spectrum is converted to the linear power spectrum by performing uniform resampling. However, the information from the octave power spectrum is different from that carried by the linear power spectrum. We believe that the octave power spectrum can offer complementary information to the linear power spectrum for spoofing attack detection. In this regard, we propose to combine the coefficients generated using both linear and octave power spectrum. The combined feature is referred to as extended CQCC (eCQCC), which is hypothesized to have better discriminative information for detection of spoofing attacks. In addition, we use the short-term spectral statistics information (STSSI) along with eCQCC feature to form another novel feature representation referred to as eCQCC-STSSI to have improved anti-spoofing countermeasure. We perform the studies with the proposed features for both synthetic and playback attacks using ASVspoof 2015 and ASVspoof 2017 version 2.0 corpus, respectively. The studies reveal that eCQCC outperforms the conventional CQCC feature as well most of the known systems showing importance of octave spectrum information. Further, the hybrid feature eCQCC-STSSI improve the performance of eCQCC feature due to the STSSI information combined with it.

Development of Machine Learning for Asthmatic and Healthy Voluntary Cough Sounds: A Proof of Concept Study

(1) Background: Cough is a major presentation in childhood asthma. Here, we aim to develop a machine-learning based cough sound classifier for asthmatic and healthy children. (2) Methods: Children less than 16 years old were randomly recruited in a Children’s Hospital, from February 2017 to April 2018, and were divided into 2 cohorts—healthy children and children with acute asthma presenting with cough. Children with other concurrent respiratory conditions were excluded in the asthmatic cohort. Demographic data, duration of cough, and history of respiratory status were obtained. Children were instructed to produce voluntary cough sounds. These clinically labeled cough sounds were randomly divided into training and testing sets. Audio features such as Mel-Frequency Cepstral Coefficients and Constant-Q Cepstral Coefficients were extracted. Using a training set, a classification model was developed with Gaussian Mixture Model–Universal Background Model (GMM-UBM). Its predictive performance was tested using the test set against the physicians’ labels. (3) Results: Asthmatic cough sounds from 89 children (totaling 1192 cough sounds) and healthy coughs from 89 children (totaling 1140 cough sounds) were analyzed. The sensitivity and specificity of the audio-based classification model was 82.81% and 84.76%, respectively, when differentiating coughs from asthmatic children versus coughs from ‘healthy’ children. (4) Conclusion: Audio-based classification using machine learning is a potentially useful technique in assisting the differentiation of asthmatic cough sounds from healthy voluntary cough sounds in children.

A Comparison of Features for Replay Attack Detection

Speaker verification (ASV) systems are still vulnerable to different kinds of spoofing attacks, especially replay attack due to high-quality playback devices. Many countermeasures have been developed recently. Most of the efforts focus on the search for more salient features and many new features have been proposed. Five kinds of features, namely Mel-frequency cepstral coefficients (MFCCs), linear frequency cepstral coefficients (LFCCs), inverted Mel-frequency cepstral coefficients (IMFCCs), constant Q cepstral coefficients (CQCCs) and bottleneck features were compared on the public ASVspoof 2017 and BTAS 2016 datasets in this paper. Our experimental results show that MFCCs and bottleneck features yield comparable results. Both of them significantly outperform others (including the recently proposed CQCCs). However, the number of filters and cepstral bins are essential to the success of MFCCs.

Low frequency frame-wise normalization over constant-Q transform for playback speech detection

The playback speech contains information from the environment, playback and recorder used. This work focuses on proposal of a novel normalization scheme, namely, low frequency frame-wise normalization (LFFN) as one of the modules in feature extraction process that is hypothesized to help in capturing the artifacts from the playback speech. It is based on low frequency bin processing that is performed frame-wise and hence its name. The constant-Q transform (CQT) based features are found to provide the benchmark results for detection of spoofing attacks. In this work, LFFN is combined with CQT to extract two new features from octave and linear power spectra, respectively. The first one is obtained by CQT, LFFN and octave segmentation that is referred to as constant-Q normalization segmentation coefficients (CQNSC). The latter uses conventional constant-Q cepstral coefficient (CQCC) and LFFN to obtain constant-Q normalization cepstral coefficients (CQNCC). The studies are performed on ASVspoof 2017 version 2.0 corpus that is designed for studying playback speech detection. The experimental results show the effectiveness of proposed LFFN with CQT based features. We obtain equal error rate of 10.63% and 10.31% for CQNSC and CQNCC features on the evaluation set of ASVspoof 2017 version 2.0 corpus, respectively.

Spoofing Detection in Automatic Speaker Verification Systems Using DNN Classifiers and Dynamic Acoustic Features.

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.

Data selection for i-vector based automatic speaker verification anti-spoofing

State-of-the-art i-vector based automatic speaker verification (ASV) systems lead to considerably high performance and thus voice becomes one of the most important biometric modality for person authentication. However, similar to other biometrics, ASV systems are highly vulnerable to spoofing attacks. Therefore, developing countermeasures for detecting spoofing attacks plays an important role against the concerns regarding the reliability of ASV systems. Recent studies have shown that simple Gaussian mixture model (GMM) classifier outperforms i-vector countermeasures. In this study, we focus on improving the spoofing detection performance of i-vector system using cosine and probabilistic linear discriminant analysis (PLDA) scoring. Experimental results conducted on ASVspoof 2015 database reveals that the data used to train the two key elements of i-vector system, universal background model (UBM) and the i-vector extractor (T-matrix), play an important role on spoofing detection performance. In this paper, we study the effect of using different type of data (genuine/human or spoofed) to train these two elements and their performance on spoofing detection. In particular, extracting i-vectors using UBM trained with genuine (human) speech utterances and T-matrix trained from both genuine and spoofed utterances leads to 50% performance improvement on spoofing detection. With the proposed scheme, unlike the previous results, i-vector countermeasure outperforms GMM classifier. Finally, experimental results shows that recently proposed constant Q cepstral coefficients (CQCC) shows superior performance in comparison to standard Mel-frequency cepstral coefficients (MFCC).

Synthetic speech detection using fundamental frequency variation and spectral features

Recent works on the vulnerability of automatic speaker verification (ASV) systems confirm that malicious spoofing attacks using synthetic speech can provoke significant increase in false acceptance rate. A reliable detection of synthetic speech is key to develop countermeasure for synthetic speech based spoofing attacks. In this paper, we targeted that by focusing on three major types of artifacts related to magnitude, phase and pitch variation, which are introduced during the generation of synthetic speech. We proposed a new approach to detect synthetic speech using score-level fusion of front-end features namely, constant Q cepstral coefficients (CQCCs), all-pole group delay function (APGDF) and fundamental frequency variation (FFV). CQCC and APGDF were individually used earlier for spoofing detection task and yielded the best performance among magnitude and phase spectrum related features, respectively. The novel FFV feature introduced in this paper to extract pitch variation at frame-level, provides complementary information to CQCC and APGDF. Experimental results show that the proposed approach produces the best stand-alone spoofing detection performance using Gaussian mixture model (GMM) based classifier on ASVspoof 2015 evaluation dataset. An overall equal error rate of 0.05% with a relative performance improvement of 76.19% over the next best-reported results is obtained using the proposed method. In addition to outperforming all existing baseline features for both known and unknown attacks, the proposed feature combination yields superior performance for ASV system (GMM with universal background model/i-vector) integrated with countermeasure framework. Further, the proposed method is found to have relatively better generalization ability when either one or both of copy-synthesized data and limited spoofing data are available a priori in the training pool.

Constant Q cepstral coefficients: A spoofing countermeasure for automatic speaker verification

Recent evaluations such as ASVspoof 2015 and the similarly-named AVspoof have stimulated a great deal of progress to develop spoofing countermeasures for automatic speaker verification. This paper reports an approach which combines speech signal analysis using the constant Q transform with traditional cepstral processing. The resulting constant Q cepstral coefficients (CQCCs) were introduced recently and have proven to be an effective spoofing countermeasure. An extension of previous work, the paper reports an assessment of CQCCs generalisation across three different databases and shows that they deliver state-of-the-art performance in each case. The benefit of CQCC features stems from a variable spectro-temporal resolution which, while being fundamentally different to that used by most automatic speaker verification system front-ends, also captures reliably the tell-tale signs of manipulation artefacts which are indicative of spoofing attacks. The second contribution relates to a cross-database evaluation. Results show that CQCC configuration is sensitive to the general form of spoofing attack and use case scenario. This finding suggests that the past single-system pursuit of generalised spoofing detection may need rethinking.