A number of AI-generated tools are used today to clone human voices, leading to a new technology known as Audio Deepfakes (ADs). Despite being introduced to enhance human lives as audiobooks, ADs have been used to disrupt public safety. ADs have thus recently come to the attention of researchers, with Machine Learning (ML) and Deep Learning (DL) methods being developed to detect them. In this article, a review of existing AD detection methods was conducted, along with a comparative description of the available faked audio datasets. The article introduces types of AD attacks and then outlines and analyzes the detection methods and datasets for imitation- and synthetic-based Deepfakes. To the best of the authors’ knowledge, this is the first review targeting imitated and synthetically generated audio detection methods. 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. 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. 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.

One of the most significant discussions in forensics is Audio Deepfake, where AI-generated tools are used to clone audio content of people’s voices. Although it was intended to improve people’s lives, attackers utilized it maliciously, compromising the public’s safety. Thus, Machine Learning (ML) and Deep Learning (DL) methods have been developed to detect imitated or synthetically faked voices. However, the developed methods suffered from massive training data or excessive pre-processing. To the author’s best knowledge, Arabic speech has not yet been explored with synthetic fake audio, and it is very limited to the challenged fakeness, which is imitation. This paper proposed a new Audio Deepfake detection method called Arabic-AD based on self-supervised learning techniques to detect both synthetic and imitated voices. Additionally, it contributed to the literature by creating the first synthetic dataset of a single speaker who perfectly speaks Modern Standard Arabic (MSA). Besides, the accent was also considered by collecting Arabic recordings from non-Arabic speakers to evaluate the robustness of Arabic-AD. Three extensive experiments were conducted to measure the proposed method and compare it to well-known benchmarks in literature. As a result, Arabic-AD outperformed other state-of-the-arts methods with the lowest EER rate (0.027%), and high detection accuracy (97%) while avoiding the need for excessive training.

Zero-shot classification presents a challenge since it necessitates a model to categorize images belonging to classes it has not encountered during its training phase. Previous research in the field of remote sensing (RS) has explored this task by training image-based models on known RS classes and then attempting to predict the outcomes for unfamiliar classes. Despite these endeavors, the outcomes have proven to be less than satisfactory. In this paper, we propose an alternative approach that leverages vision-language models (VLMs), which have undergone pre-training to grasp the associations between general computer vision image-text pairs in diverse datasets. Specifically, our investigation focuses on thirteen VLMs derived from Contrastive Language-Image Pre-Training (CLIP/Open-CLIP) with varying levels of parameter complexity. In our experiments, we ascertain the most suitable prompt for RS images to query the language capabilities of the VLM. Furthermore, we demonstrate that the accuracy of zero-shot classification, particularly when using large CLIP models, on three widely recognized RS scene datasets yields superior results compared to existing RS solutions.

Due to an increase in the number of disabled people around the world, inclusive solutions are becoming a priority. People with disabilities may encounter many problems and may not be able to easily participate in various activities due to physical barriers, which may sometimes cause them to be frustrated and embarrassed. Recently, the emerging telepresence robot technology has been proposed to enable people with disabilities to increase their presence by incorporating information and communications technology (ICT) into robotics platforms. Therefore, in this paper we conduct a comprehensive analysis using comparative and elicitation studies to understand the current state of mobile telepresence robot systems and to identify the gaps that must be filled. This paper further contributes to the literature by proposing a novel telepresence robot system that adapts text-to-speech (TTS) and ICT technologies with robotics for its use as an assistant. To the authors’ knowledge, the proposed system is the first MRP system that supports speech impairment and introduces emotion components into its communication function. It includes an operator site (mobile) and a remote site (robot) to allow users to control the robot from a distance and communicate with others in remote locations. It allows the user to physically interact with people and show certain emotions through the robot in remote locations, or it can accompany them to speak on their behalf. It can provide agency for both remote and in-class users through emoji-based communication and audio–video streaming with recording functionality. As shown at the end of this paper, the system was tested with 30 people, some of whom had mobility or speech disabilities, showing that the user acceptance score was above 95% and that people with disabilities liked to interact with other people using the proposed system. The users appreciated having the ability to control the robot from a distance and praised the capability to show their emotions through the robot emoji motions and to control the audio–video streaming. From this study, we conclude that the proposed telepresence system could be an asset to people with speech and mobility disabilities and could help them feel physically present in various places.

Due to the rapid developments in technology and the sudden expansion of social media use, Dialect Arabic has become an important source of data that needs to be addressed when building Arabic corpora. In this paper, thirty-three Arabic corpora are surveyed to show that despite all of the developments in the literature, Saudi dialect (SD) corpora still need further expansion. This paper contributes to the literature on SD corpora by creating the largest Saudi corpus - the King Saud University Saudi Corpus (KSUSC) - with +1B total words, including +119M SD words. The KSUSC not only is the newest and largest SD corpus but is also diverse, covering 26 domains in text collected from five different sources. This paper also contributes to the literature by developing a new incremental preprocessing system that is used to create relevant lexicons that are then used to clean and normalize the collected data. This incremental system is scalable and can be adapted for different resources and dialects. Moreover, the collection process for building the KSUSC is discussed in detail, and the challenges in collecting SD text with respect to each platform are highlighted. By the end of this paper, different design criteria are proposed and used with the KSUSC to conclude that the resulting corpus can be of great benefit to researchers who are interested in integrating the corpus with their own work or using its resulting lexicons with Saudi-based NLP tasks.