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This work presents a novel audio features descriptor named as extended local ternary pattern (ELTP) to capture the vocal tract dynamically induced attributes of bonafide speech and algorithmic artifacts in synthetic and converted speeches. Thus, there exists a need to develop an effective voice spoofing countermeasure that can reliably be used to protect these VDDs against such malicious attacks. Intruders can exploit these attacks to bypass the security of such systems and gain access of victim’s bank account or home control.
#Deepfake text to speech free verification#
However, these VDDs that are based on automatic speaker verification systems (ASVs) are vulnerable to voice based logical access (LA) attacks like Text-to-Speech (TTS) synthesis and converted voice signals. Voice-driven devices (VDDs) like Google Home and Amazon Alexa, which are well-known connected devices in consumer IoT, have applications in various domains i.e., home appliances automation, next-generation vehicles, voice banking, and so on. We can say that our results can be used by the experts and researchers for building better intelligent learning support systems in Arabic speech processing. Given a set of unseen audio samples of uttered short vowels, our proposed model has reached the testing accuracy of 95.77%. The model is constructed from scratch by: (i) collecting a new audio dataset, (ii) developing a neural network architecture, and (iii) optimizing and fine-tuning the developed model through several iterations to achieve high classification accuracy. Therefore, we developed a model that classifies Arabic short vowels using Deep Neural Networks (DNN). A major challenge in the recognition of precise pronunciation of Arabic alphabets is the correct identification of a large number of short vowels, which cannot be dealt with using traditional statistical audio processing techniques and machine learning models. In our case, the task is about recognizing the exact pronunciation of Arabic alphabets according to the standards. Given that online learning has become a norm these days, modern learning requires assessment by virtual teachers. An intelligent process of students’ evaluation can make learning and teaching easier for both students and teachers. That makes the teaching and learning task cumbersome for both parties. For this reason, both the students and teachers of Classical Arabic (CA) are required extra practice for correcting students’ pronunciation of Arabic short vowels. This article can be a starting point for researchers to understand the current state of the AD literature and investigate more robust detection models that can detect fakeness even if the target audio contains accented voices or real-world noises.Ī mispronunciation of Arabic short vowels can change the meaning of a complete sentence. Moreover, at the end of this article, the potential research directions and challenges of Deepfake detection methods are discussed to discover that, even though AD detection is an active area of research, further research is still needed to address the existing gaps. The similarities and differences of AD detection methods are summarized by providing a quantitative comparison that finds that the method type affects the performance more than the audio features themselves, in which a substantial tradeoff between the accuracy and scalability exists. To the best of the authors' knowledge, this is the first review targeting imitated and synthetically generated audio detection methods. The article introduces types of AD attacks and then outlines and analyzes the detection methods and datasets for imitation-and synthetic-based Deepfakes. In this article, a review of existing AD detection methods was conducted, along with a comparative description of the available faked audio datasets.
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ADs have thus recently come to the attention of researchers, with Machine Learning (ML) and Deep Learning (DL) methods being developed to detect them. Despite being introduced to enhance human lives as audiobooks, ADs have been used to disrupt public safety. A number of AI-generated tools are used today to clone human voices, leading to a new technology known as Audio Deepfakes (ADs).
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