Deepfakes, particularly in the auditory domain, have become a significant threat, necessitating the development of robust countermeasures. This paper addresses the escalating challenges posed by deepfake attacks on Automatic Speaker Verification (ASV) systems. We present a novel Urdu deepfake audio dataset for deepfake detection, focusing on two spoofing attacks – Tacotron and VITS TTS. The dataset construction involves careful consideration of phonemic cover and balance and comparison with existing corpora like PRUS and PronouncUR. Evaluation with AASIST-L model shows EERs of 0.495 and 0.524 for VITS TTS and Tacotron-generated audios, respectively, with variability across speakers. Further, this research implements a detailed human evaluation, incorporating a user study to gauge whether people are able to discern deepfake audios from real (bonafide) audios. The ROC curve analysis shows an area under the curve (AUC) of 0.63, indicating that individuals demonstrate a limited ability to detect deepfakes (approximately 1 in 3 fake audio samples are regarded as real). Our work contributes a valuable resource for training deepfake detection models in low-resource languages like Urdu, addressing the critical gap in existing datasets. The dataset is publicly available at: https://github.com/CSALT-LUMS/urdu-deepfake-dataset.

Recent advancements in Neural Machine Translation (NMT) systems have significantly improved model performance on various translation benchmarks. However, these systems still face numerous challenges when translating low-resource languages such as Urdu. In this work, we highlight the specific issues faced by machine translation systems when translating Urdu language. We first conduct a comprehensive evaluation of English to Urdu Machine Translation with four diverse models: GPT-3.5 (a large language model), opus-mt-en-ur (a bilingual translation model), NLLB (a model trained for translating 200 languages), and IndicTrans2 (a specialized model for translating low-resource Indic languages). The results demonstrate that IndicTrans2 significantly outperforms other models in Urdu Machine Translation. To understand the differences in the performance of these models, we analyze the Urdu word distribution in different training datasets and compare the training methodologies. Finally, we uncover the specific translation issues and provide suggestions for improvements in Urdu machine translation systems.

Model pruning methods reduce memory requirements and inference time of large-scale pre-trained language models after deployment. However, the actual pruning procedure is computationally intensive, involving repeated training and pruning until the required sparsity is achieved. This paper combines data pruning with movement pruning for Neural Machine Translation (NMT) to enable efficient fine-pruning. We design a dataset pruning strategy by leveraging cross-entropy scores of individual training instances. We conduct pruning experiments on the task of machine translation from Romanian-to-English and Turkish-to-English, and demonstrate that selecting hard-to-learn examples (top-k) based on training cross-entropy scores outperforms other dataset pruning methods. We empirically demonstrate that data pruning reduces the overall steps required for convergence and the training time of movement pruning. Finally, we perform a series of experiments to tease apart the role of training data during movement pruning and uncover new insights to understand the interplay between data and model pruning in the context of NMT.