The process of detecting language from an audio clip by an unknown speaker, regardless of gender, manner of speaking, and distinct age speaker, is defined as spoken language identification (SLID). The considerable task is to recognize the features that can distinguish between languages clearly and efficiently. The model uses audio files and converts those files into spectrogram images. It applies the convolutional neural network (CNN) to bring out main attributes or features to detect output easily. The main objective is to detect languages out of English, French, Spanish, and German, Estonian, Tamil, Mandarin, Turkish, Chinese, Arabic, Hindi, Indonesian, Portuguese, Japanese, Latin, Dutch, Portuguese, Pushto, Romanian, Korean, Russian, Swedish, Tamil, Thai, and Urdu. An experiment was conducted on different audio files using the Kaggle dataset named spoken language identification. These audio files are comprised of utterances, each of them spanning over a fixed duration of 10 seconds. The whole dataset is split into training and test sets. Preparatory results give an overall accuracy of 98%. Extensive and accurate testing show an overall accuracy of 88%.

The development of deep convolutional neural networks has been largely responsible for the significant strides forward made in steganography over the past decade. In the field of image steganography, generative adversarial networks (GAN) are becoming increasingly popular. This study describes current development in image steganographic systems based on deep learning. The authors' goal is to lay out the various works that have been done in image steganography using deep learning techniques and provide some notes on the various methods. This study proposed a result that could open up some new avenues for future research in deep learning based on image steganographic methods. These new avenues could be explored in the future. Moreover, the pros and cons of current methods are laid out with several promising directions to define problems that researchers can work on in future research avenues.

Restoration of a 3D face from the mesh image is highly demanded in computer vision applications. 3D face restoration is a challenging task due to the variation of expression, poses, intrinsic geometries, and textures. The proposed technique consists of two main components, namely face restoration and recognition. A novel three‐dimensional (3D) landmark‐based face restoration method is proposed. 3D facial landmarks are used in the face recognition technique. It uses the principle of reflection and mid‐face plane for the restoration of facial landmarks. By using the restored 3D face, a deep learning‐based face recognition system is developed. It utilizes the concept of deep features from variational autoencoders. Further, these deep feature embeddings are trained using triplet loss training to increase the distance between embeddings of different persons and decreasing the distance between embeddings of the same person. These trained embeddings are used in support vector machine for prediction. The proposed framework is compared with recently developed face recognition techniques in terms of computational time. The proposed technique is able to recognize the person's face with better accuracy than the existing methods. Further, ablation studies are conducted to test the robustness of the proposed technique.