Using Kernel Discriminant Analysis to Improve the Characterization of the Alternative Hypothesis for Speaker Verification

Chao, Yi-Hsiang; Tsai, Wei-Ho; Wang, Hsin‐Min; Chang, Ruei-Chuan

doi:10.1109/tasl.2008.2004297

Cited by 9 publications

(3 citation statements)

References 21 publications

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“…A support vector machine-based classifier was used for speaker verification utilizing the Gaussian mixture model (GMM) [4]. They achieved an equivalent error rate of 4.92% and 7.78% in the 2006 NIST speaker recognition evaluation test.…”

Section: Related Workmentioning

confidence: 99%

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Fake speech detection using VGGish with attention block

Kanwal,

Mahum,

AlSalman

et al. 2024

J AUDIO SPEECH MUSIC PROC.

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While deep learning technologies have made remarkable progress in generating deepfakes, their misuse has become a well-known concern. As a result, the ubiquitous usage of deepfakes for increasing false information poses significant risks to the security and privacy of individuals. The primary objective of audio spoofing detection is to identify audio generated through numerous AI-based techniques. Several techniques for fake audio detection already exist using machine learning algorithms. However, they lack generalization and may not identify all types of AI-synthesized audios such as replay attacks, voice conversion, and text-to-speech (TTS). In this paper, a deep layered model, i.e., VGGish, along with an attention block, namely Convolutional Block Attention Module (CBAM) for spoofing detection, is introduced. Our suggested model successfully classifies input audio into two classes: Fake and Real, converting them into mel-spectrograms, and extracting their most representative features due to the attention block. Our model is a significant technique to utilize for audio spoofing detection due to a simple layered architecture. It captures complex relationships in audio signals due to both spatial and channel features present in an attention module. To evaluate the effectiveness of our model, we have conducted in-depth testing using the ASVspoof 2019 dataset. The proposed technique achieved an EER of 0.52% for Physical Access (PA) attacks and 0.07 % for Logical Access (LA) attacks.

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Section: Related Workmentioning

confidence: 99%

“…Research bodies and individuals have suggested numerous strategies to combat audio spoofing assaults [3]. Moreover, advanced MLbased approaches are employed to differentiate audio using knowledge-based and data-driven countermeasures [4].…”

Section: Introductionmentioning

confidence: 99%

Fake speech detection using VGGish with attention block

Kanwal,

Mahum,

AlSalman

et al. 2024

J AUDIO SPEECH MUSIC PROC.

View full text Add to dashboard Cite

show abstract

“…Thus, various machine learning and deep learning-based works have been proposed to detect forged speech. In [17], an SVM-based classifier has been utilized as AVS employing GMM. They attained an equal error rate of 4.92% and 7.78% on the 2006 NIST for speaker identification core test.…”

Section: Iiirelated Workmentioning

confidence: 99%

EDL-Det: A Robust TTS Synthesis Detector Using VGG19-Based YAMNet and Ensemble Learning Block

Mahum,

Irtaza,

Javed

2023

IEEE Access

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Various algorithms exist for the audio deep fake synthesis, such as deep voice, tacotron, fastspeech, and imitation techniques. Despite the existence of various spoofing speech detectors, they are not ready to distinguish unseen audio samples with high precision. In this study, we suggest a robust model, namely Ensemble Deep Learning based Detector (EDL-Det) to detect text-to-speech (TTS) and categorize it into spoofed and bonafide classes. Our proposed model is an improved method based on YAMNet employing VGG19 as a base network instead of MobileNet combined with two other deep learning(DL) methods. Our proposed system effectively analyzes the mel-spectrograms generated from input audio to extract the better artifacts underlying the audio signals. We have added an ensemble learning block that consists of ResNet50, and InceptionNetv2. First, we convert speech into mel-spectrograms that consist of time-frequency representations. Second, we train our model using the ASVspoof-2019 dataset. In the end, we classified the audios converting them into mel-spectrograms using our trained binary classifier along with a majority voting scheme by three networks. Due to deep convolutional network architecture, our proposed model effectively extracts the most representative features from the mel-spectrograms. Furthermore, we have performed extensive experiments to assess the performance of the suggested model using the ASVspoof 2019 corpus. Additionally, our proposed model is robust enough to identify the unseen spoofed audios and accurately classify the attacks based on cloning algorithms.

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