Constant Q Cepstral Coefficients and Long Short-Term Memory Model-Based Automatic Speaker Verification System

Mittal, Aakshi; Dua, Mohit

doi:10.1007/978-981-15-8443-5_76

Cited by 10 publications

(4 citation statements)

References 21 publications

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“…Todisco et al [28] used CQCC feature extraction technique with two GMM, a binary classifier which was used to classify audios as genuine or spoof. Mittal et al used CQCC with CNN, LSTM, and a combination of the staticdynamic features of CQCC with LSTM-CNN ensemble in their proposed works of [3], [29]and [30], respectively. However, the issue of noise remains open with MFCC and CQCC features.…”

Section: A Related Workmentioning

confidence: 99%

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Spoof Detection using Sequentially Integrated Image and Audio Features

Chakravarty¹,

Dua²

2023

IJCDS

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Analyzing the intricate nature of an audio signal often requires the extraction of relevant features, which serve as informative descriptors of the signal. It entails studying the signal and determining how signals are related to one another. As a result, the performance of audio spoofing detection in Automatic Speaker Verification (ASV) systems is strongly reliant on front-end feature extraction. In this paper, three types of successively integrated features have been proposed. First, Acoustic Ternary Pattern (ATP) image features are sequentially fused with different audio features such as MFCC, CQCC, GTCC, BFCC and PLP, individually. Second, LBP image features are combined with all these audio features similarly. Then, the sequential integration of ATP-LBP features is combined individually with MFCC, CQCC, GTCC, BFCC and PLP features. Finally, these front-end hybrid feature sets are classified using different ML and deep learning algorithms based acoustic models at the back-end. The state-of-the-art ASVspoof 2019 dataset has been used to implement various front-end and back-end combinations. The research outcomes reveal that the proposed approach achieved the best results with ATP-LBP-GTCC at the front end with LSTM-based acoustic model at the back-end.

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Section: A Related Workmentioning

confidence: 99%

“…Motivated by the works of [20], [13] and [3], [29], [30], the proposed work in this paper suggests a novel integrated approach for front end feature extraction. The proposed approach uses combination of audio and image feature extraction techniques.…”

Section: A Related Workmentioning

confidence: 99%

Spoof Detection using Sequentially Integrated Image and Audio Features

Chakravarty¹,

Dua²

2023

IJCDS

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“…Mittal et al [33] utilized CQCC at the front-end and employed two back-end models, CNN and LSTM, both individually and in combination as LSTM-CNN in their proposed work [34,35] and [33,36], respectively. Khochare et al [37] proposed a model for detecting deep fake audios, and for implementing this work fake or real datasets [38] have been used.…”

Section: Literature and Contributionmentioning

confidence: 99%

Data augmentation and hybrid feature amalgamation to detect audio deep fake attacks

Chakravarty

Dua

2023

Phys. Scr.

Self Cite

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The ability to distinguish between authentic and fake audio is become increasingly difficult due to the increasing accuracy of text-to-speech models, posing a serious threat to speaker verification systems. Furthermore, audio deepfakes are becoming a more likely source of deception with the development of sophisticated methods for producing synthetic voice. The ASVspoof dataset has recently been used extensively in research on the detection of audio deep fakes, together with a variety of machine and deep learning methods. The proposed work in this paper combines data augmentation techniques with hybrid feature extraction method at front-end. Two variants of audio augmentation method and Synthetic Minority Over Sampling Technique (SMOTE) have been used, which have been combined individually with Mel Frequency Cepstral Coefficients (MFCC), Gammatone Cepstral Coefficients (GTCC) and hybrid these two feature extraction methods for implementing front-end feature extraction. To implement the back-end our proposed work two deep learning models, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), and two Machine Learning (ML) classifier Random Forest (RF) and Support Vector Machine (SVM) have been used. For training, and evaluation ASVspoof 2019 Logical Access (LA) partition, and for testing of the said systems, and ASVspoof 2021 deep fake partition have been used. After analysing the results, it can be observed that combination of MFCC+GTCC with SMOTE at front-end and LSTM at back-end has outperformed all other models with 99% test accuracy, and 1.6 % Equal Error Rate (EER) over deepfake partition. Also, the testing of this best combination has been done on DEepfake CROss-lingual (DECRO) dataset. To access the effectiveness of proposed model under noisy scenarios, we have analysed our best model under noisy condition by adding Babble Noise, Street Noise and Car Noise to test data.

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“…In various speech and speaker recognition tasks, LSTM-based deep learning mod-els are performing better than the other models. However, CNN models are also giving satisfactory results [31][32][33]. Also, different arrangements of frontend and backend models can bring smoothness and accuracy to spoof detection task.…”

Section: Introductionmentioning

confidence: 99%

Static–dynamic features and hybrid deep learning models based spoof detection system for ASV

Mittal

Dua

2021

Complex Intell. Syst.

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Detection of spoof is essential for improving the performance of current scenario of Automatic Speaker Verification (ASV) systems. Empowerment to both frontend and backend parts can build the robust ASV systems. First, this paper discuses performance comparison of static and static–dynamic Constant Q Cepstral Coefficients (CQCC) frontend features by using Long Short Term Memory (LSTM) with Time Distributed Wrappers model at the backend. Second, it performs comparative analysis of ASV systems built using three deep learning models LSTM with Time Distributed Wrappers, LSTM and Convolutional Neural Network at backend and using static–dynamic CQCC features at frontend. Third, it discusses implementation of two spoof detection systems for ASV by using same static–dynamic CQCC features at frontend and different combination of deep learning models at backend. Out of these two, the first one is a voting protocol based two-level spoof detection system that uses CNN, LSTM model at first level and LSTM with Time Distributed Wrappers model at second level. The second one is a two-level spoof detection system with user identification and verification protocol, which uses LSTM model for user identification at first level and LSTM with Time Distributed Wrappers for verification at the second level. For implementing the proposed work, a variation in ASVspoof 2019 dataset has been used to introduce all types of spoofing attacks such as Speech Synthesis (SS), Voice Conversion (VC) and replay in single set of dataset. The results show that, at frontend, static–dynamic CQCC feature outperform static CQCC features and at the backend, hybrid combination of deep learning models increases accuracy of spoof detection systems.

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Constant Q Cepstral Coefficients and Long Short-Term Memory Model-Based Automatic Speaker Verification System

Cited by 10 publications

References 21 publications

Spoof Detection using Sequentially Integrated Image and Audio Features

Spoof Detection using Sequentially Integrated Image and Audio Features

Data augmentation and hybrid feature amalgamation to detect audio deep fake attacks

Static–dynamic features and hybrid deep learning models based spoof detection system for ASV

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