Improving the potential of Enhanced Teager Energy Cepstral Coefficients (ETECC) for replay attack detection

Patil, Ankur T.; Acharya, Rajul; Patil, Hemant A.; Guido, Rodrigo Capobianco

doi:10.1016/j.csl.2021.101281

Cited by 17 publications

(9 citation statements)

References 59 publications

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“…The dysarthric TEO profile was found to be highly irregular, with more high amplitude and noisy bumps noted at high severity levels. Later in [44], it was shown that the energy estimated using the enhanced Teager energy operator (ETEO) [45], is greater than that obtained by using the TEO for higher frequencies for normal speech. Inspired by these results, we would like to explore the usage of ETEO for differentiating the different dysarthria severity levels as future work.…”

Section: Discussionmentioning

confidence: 99%

Automated Dysarthria Severity Classification: A Study on Acoustic Features and Deep Learning Techniques

Joshy

Rajan

2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Assessing the severity level of dysarthria can provide an insight into the patient's improvement, assist pathologists to plan therapy, and aid automatic dysarthric speech recognition systems. In this article, we present a comparative study on the classification of dysarthria severity levels using different deep learning techniques and acoustic features. First, we evaluate the basic architectural choices such as deep neural network (DNN), convolutional neural network, gated recurrent units and long short-term memory network using the basic speech features, namely, Mel-frequency cepstral coefficients (MFCCs) and constant-Q cepstral coefficients. Next, speech-disorder specific features computed from prosody, articulation, phonation and glottal functioning are evaluated on DNN models. Finally, we explore the utility of low-dimensional feature representation using subspace modeling to give i-vectors, which are then classified using DNN models. Evaluation is done using the standard UA-Speech and TORGO databases. By giving an accuracy of 93.97% under the speaker-dependent scenario and 49.22% under the speaker-independent scenario for the UA-Speech database, the DNN classifier using MFCC-based i-vectors outperforms other systems.

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Section: Discussionmentioning

confidence: 99%

Automated Dysarthria Severity Classification: A Study on Acoustic Features and Deep Learning Techniques

Joshy

Rajan

2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…The EER of the evaluation and development sets were 10.75% and 5.55%, respectively. The authors in [19] evaluated a spectrum analyzer referred to as 'cochlear' which comprises of a level-dependent compression and a sharp frequency tuning. They then created a method by using an adaptive notch and resonant filter for the cochlear model.…”

Section: Literature Reviewmentioning

confidence: 99%

Voice spoofing countermeasure for voice replay attacks using deep learning

et al. 2022

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In our everyday lives, we communicate with each other using several means and channels of communication, as communication is crucial in the lives of humans. Listening and speaking are the primary forms of communication. For listening and speaking, the human voice is indispensable. Voice communication is the simplest type of communication. The Automatic Speaker Verification (ASV) system verifies users with their voices. These systems are susceptible to voice spoofing attacks - logical and physical access attacks. Recently, there has been a notable development in the detection of these attacks. Attackers use enhanced gadgets to record users’ voices, replay them for the ASV system, and be granted access for harmful purposes. In this work, we propose a secure voice spoofing countermeasure to detect voice replay attacks. We enhanced the ASV system security by building a spoofing countermeasure dependent on the decomposed signals that consist of prominent information. We used two main features— the Gammatone Cepstral Coefficients and Mel-Frequency Cepstral Coefficients— for the audio representation. For the classification of the features, we used Bi-directional Long-Short Term Memory Network in the cloud, a deep learning classifier. We investigated numerous audio features and examined each feature’s capability to obtain the most vital details from the audio for it to be labelled genuine or a spoof speech. Furthermore, we use various machine learning algorithms to illustrate the superiority of our system compared to the traditional classifiers. The results of the experiments were classified according to the parameters of accuracy, precision rate, recall, F1-score, and Equal Error Rate (EER). The results were 97%, 100%, 90.19% and 94.84%, and 2.95%, respectively.

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“…The EER of the evaluation and development sets were 10.75% and 5.55%, respectively. The authors in [15] evaluated a spectrum analyzer referred to as 'cochlear' which comprises of a level-dependent compression and a sharp frequency tuning. They then created a method by using an adaptive notch and resonant filter for the cochlear model.…”

Section: Literature Reviewmentioning

confidence: 99%

Voice Spoofing Countermeasure for Voice Replay Attacks using Deep Learning

Zhou

Tao

Jawawi

et al. 2022

Preprint

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In our everyday lives, we communicate with each other using several means and channels of communication, as communication is crucial in the lives of humans. Listening and speaking are the primary forms of communication. For listening and speaking, the human voice is indispensable. Voice communication is the simplest type of communication. The Automatic Speaker Verification (ASV) system verifies users with their voices. These systems are susceptible to voice spoofing attacks - logical and physical access attacks. Recently, there has been a notable development in the detection of these attacks. Attackers use enhanced gadgets to record users' voices, replay it for the ASV system, and be granted access for harmful purposes. In this work, we propose a secure voice spoofing countermeasure for the purpose of detecting voice replay attacks. We enhanced the ASV system security by building a spoofing countermeasure dependent on the decomposed signals that consists of prominent information. We used two main features— the Gammatone Cepstral Coefficients and Mel-Frequency Cepstral Coefficients— for the audio representation. For the classification of the features, we used Bi-directional Long-Short Term Memory Network in cloud, a deep learning classifier. We investigated numerous audio features and examined each feature’s capability to obtain the most vital details from the audio for it to be labelled genuine or a spoof speech. Furthermore, we use various machine learning algorithms to illustrate the superiority of our system compared to the traditional classifiers. The results of the experiments were classified according to the parameters of accuracy, precision rate, recall, F1-score, and Equal Error Rate (EER). The results were 97\%, 100\%, 90.19\% and 94.84\%, and 2.95\%, respectively.

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Improving the potential of Enhanced Teager Energy Cepstral Coefficients (ETECC) for replay attack detection

Cited by 17 publications

References 59 publications

Automated Dysarthria Severity Classification: A Study on Acoustic Features and Deep Learning Techniques

Automated Dysarthria Severity Classification: A Study on Acoustic Features and Deep Learning Techniques

Voice spoofing countermeasure for voice replay attacks using deep learning

Voice Spoofing Countermeasure for Voice Replay Attacks using Deep Learning

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