Multilingual Voice Impersonation Dataset and Evaluation

Mandalapu, Hareesh; Ramachandra, Raghavendra; Busch, Christoph

doi:10.1007/978-3-030-71711-7_15

Cited by 4 publications

(6 citation statements)

References 14 publications

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“…Alternatively, one may exploit the public datasets, like voxceleb [61] that contain the original voices of celebrities; one may still vie for human mimicked version voices of these celebrities on YouTube. A related dataset is [62].…”

Section: The Datasetmentioning

confidence: 99%

Faked speech detection with zero prior knowledge

Al Ajmi,

Hayat,

Al Obaidi

et al. 2024

Discov Appl Sci

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Audio is one of the most used ways of human communication, but at the same time it can be easily misused to trick people. With the revolution of AI, the related technologies are now accessible to almost everyone, thus making it simple for the criminals to commit crimes and forgeries. In this work, we introduce a neural network method to develop a classifier that will blindly classify an input audio as real or mimicked; the word ‘blindly’ refers to the ability to detect mimicked audio without references or real sources. We propose a deep neural network following a sequential model that comprises three hidden layers, with alternating dense and drop out layers. The proposed model was trained on a set of 26 important features extracted from a large dataset of audios to get a classifier that was tested on the same set of features from different audios. The data was extracted from two raw datasets, especially composed for this work; an all English dataset and a mixed dataset (Arabic plus English). For the purpose of comparison, the audios were also classified through human inspection with the subjects being the native speakers. The ensued results were interesting and exhibited formidable accuracy, as we were able to get at least $$94\%$$ 94 % correct classification of the test cases, as against the $$85\%$$ 85 % accuracy in the case of human observers.

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Section: The Datasetmentioning

confidence: 99%

Faked speech detection with zero prior knowledge

Al Ajmi,

Hayat,

Al Obaidi

et al. 2024

Discov Appl Sci

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show abstract

“…In [48], an entirely automated pipeline that leveraged computer vision techniques was employed to create voxceleb1 data from open-source media. Investigating voice impersonation attacks and their implications for automatic speaker verification (ASV) systems was the central focus of the paper in [49]. Furthermore, the research paper in [50] thoroughly examined the encoding layers and loss functions utilized in end-to-end speaker and language recognition systems.…”

Section: Related Workmentioning

confidence: 99%

“…Specifically, for the TIMIT dataset, we followed the data split used by [46] for 630 speakers and [42,43] for 120 speakers. Similarly, for the VoxCeleb1 dataset, we employed the same data split as described in [47][48][49][50] to ensure consistency and fairness in our comparisons. This approach allowed us to conduct meaningful evaluations while maintaining parity with existing studies.…”

mentioning

confidence: 99%

Enhancing Speaker Recognition Models with Noise-Resilient Feature Optimization Strategies

Chauhan,

Isshiki,

2024

Acoustics

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This paper delves into an in-depth exploration of speaker recognition methodologies, with a primary focus on three pivotal approaches: feature-level fusion, dimension reduction employing principal component analysis (PCA) and independent component analysis (ICA), and feature optimization through a genetic algorithm (GA) and the marine predator algorithm (MPA). This study conducts comprehensive experiments across diverse speech datasets characterized by varying noise levels and speaker counts. Impressively, the research yields exceptional results across different datasets and classifiers. For instance, on the TIMIT babble noise dataset (120 speakers), feature fusion achieves a remarkable speaker identification accuracy of 92.7%, while various feature optimization techniques combined with K nearest neighbor (KNN) and linear discriminant (LD) classifiers result in a speaker verification equal error rate (SV EER) of 0.7%. Notably, this study achieves a speaker identification accuracy of 93.5% and SV EER of 0.13% on the TIMIT babble noise dataset (630 speakers) using a KNN classifier with feature optimization. On the TIMIT white noise dataset (120 and 630 speakers), speaker identification accuracies of 93.3% and 83.5%, along with SV EER values of 0.58% and 0.13%, respectively, were attained utilizing PCA dimension reduction and feature optimization techniques (PCA-MPA) with KNN classifiers. Furthermore, on the voxceleb1 dataset, PCA-MPA feature optimization with KNN classifiers achieves a speaker identification accuracy of 95.2% and an SV EER of 1.8%. These findings underscore the significant enhancement in computational speed and speaker recognition performance facilitated by feature optimization strategies.

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“…There are new kinds of attacks being generated that pose a huge threat to biometric systems in both audio [130] and face [108]. For example, voice impersonation has shown to be causing a considerable vulnerability to automatic speaker recognition [83]. However, the databases or protocols to create such attacks are not publicly available.…”

Section: ) Presentation Attack Database For Av Biometricsmentioning

confidence: 99%

Audio-Visual Biometric Recognition and Presentation Attack Detection: A Comprehensive Survey

Mandalapu

Ramachandra

et al. 2021

IEEE Access

Self Cite

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Biometric recognition is a trending technology that uses unique characteristics data to identify or verify/authenticate security applications. Amidst the classically used biometrics, voice and face attributes are the most propitious for prevalent applications in day-to-day life because they are easy to obtain through restrained and user-friendly procedures. The pervasiveness of low-cost audio and face capture sensors in smartphones, laptops, and tablets has made the advantage of voice and face biometrics more exceptional when compared to other biometrics. For many years, acoustic information alone has been a great success in automatic speaker verification applications. Meantime, the last decade or two has also witnessed a remarkable ascent in face recognition technologies. Nonetheless, in adverse unconstrained environments, neither of these techniques achieves optimal performance. Since audio-visual information carries correlated and complementary information, integrating them into one recognition system can increase the system's performance. The vulnerability of biometrics towards presentation attacks and audio-visual data usage for the detection of such attacks is also a hot topic of research. This paper made a comprehensive survey on existing state-of-the-art audio-visual recognition techniques, publicly available databases for benchmarking, and Presentation Attack Detection (PAD) algorithms. Further, a detailed discussion on challenges and open problems is presented in this field of biometrics.INDEX TERMS Biometrics, audio-visual person recognition, presentation attack detection.

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Multilingual Voice Impersonation Dataset and Evaluation

Cited by 4 publications

References 14 publications

Faked speech detection with zero prior knowledge

Faked speech detection with zero prior knowledge

Enhancing Speaker Recognition Models with Noise-Resilient Feature Optimization Strategies

Audio-Visual Biometric Recognition and Presentation Attack Detection: A Comprehensive Survey

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